From pen and paper to Artificial Intelligence: A history of network science

Title: From pen and paper to Artificial Intelligence: A history of network science.

Ensovoort, volume 43 (2022), number 7: 1

Abstract

Network science usually traces its roots to the 1700s, but it was mostly developed during the twentieth century, with a marked explosion during the past two decades. The current article traces network science’s development from early experiments to contemporary integrations with Artificial Intelligence (AI), taking into account key publications and developments in sociology, anthropology, graph theory, economics and military intelligence. The article shows how network science built upon key breakthroughs, adapted to the changing technological landscape, and became a truly interdisciplinary scientific effort. The article also touches upon recent developments in integrating AI with network science, which suggest promising future research avenues.

Keywords: systems; complexity; social network analysis; graph theory; artificial intelligence; complex networks

1. Introduction

As shown in previous studies (Senekal, 2021a, 2021b, 2021c), network theory can be considered to be a continuation of Ludwig von Bertalanffy’s General Systems Theory (GST), and was regarded as a component of GST by Von Bertalanffy himself (Von Bertalanffy, 1969:21, 90, 1972:416). Like GST, network theory is interdisciplinary by nature, and the interdisciplinary applications of network theory are reflected by its various roots, ranging from the mathematical to the sociological and anthropological. However, previous discussions of the origins of network theory focus on one development root or another. Freeman (2004), Prell (2011) and Cornelissen (2019) for instance focus on the sociological roots of network theory, while Amaral and Ottino (2004) focus on the development of graph theory. In addition, while Cornelissen (2019:20-23) notes how physicists working within graph theory often disregard the sociological and anthropological roots of network theory, most histories of network theory neglect the military intelligence branch and more recent applications of Artificial Intelligence (AI). The current article aims to provide a more diverse history of network theory that takes various roots into account.

The current article discusses the development of network theory to what Barabási (2002, 2011, 2013, 2016) calls network science, which has developed into a scientific approach in its own right. As such, the current study highlights various key developments and publications with reference to the various disciplines that contributed to this field. Suggestions are also made in terms of future research directions.

Prehistory

Network theory is closely intertwined with graph theory, a branch of mathematics, which is also generally considered to be the oldest root of network theory. The development of graph theory is mostly attributed to Leonard Euler’s famous Königsberg bridge puzzle (Boccaletti et al., 2006:177; Hu, 2012:180; Barabási, 2016:41; Cioffi-Revilla, 2017:142), as formulated in Euler (1736). Euler states the problem as follows (quoted in Amaral and Ottino (2004:151)),

In the town of Königsberg in Prussia there is an island A, called ‘Kneiphoff’, with the two branches of the river (Pregel) flowing around it. There are seven bridges, a, b, c, d, e, f, and g, crossing the two branches. The question is whether a person can plan a walk in such a way that he will cross each of these bridges once but not more than once. […] On the basis of the above I formulated the following very general problem for myself: Given any configuration of the river and the branches into which it may divide, as well as any number of bridges, to determine whether or not it is possible to cross each bridge exactly once.

Euler showed that the problem could not be solved – there is no way all bridges could be crossed only once. In the process, however, he illustrated that physical distance had nothing to do with his problem and he was the first to represent a network as a graph (Cioffi-Revilla, 2017:142).

Francois Quesnay’s (1758) study of financial networks is seen as the first network approach to economic systems (Estrada, 2011:400), and follows shortly after Leonard Euler’s Königsberg bridge puzzle (1736). Quesnay conceived of economic systems as an exchange of value in a system where components are interdependent, and most famously sketched this flow using a form of network or graph, which is reproduced and discussed in Coffman (2021).

The twentieth century

Who shall survive? (1934) by Jacob Moreno (and Helen Jennings) was “a signal event in the history of social network analysis” (Freeman, 2004:7). Moreno and Jennings investigated why 14 girls ran away from the Hudson School for Girls in New York in just two weeks, and suggested that the phenomenon had less to do with the girls’ intrinsic characteristics and more to do with their positions in a social network. Moreno used a technique he called sociometry, which graphically represented the social network in what Moreno called a sociogram (Cioffi-Revilla, 2017:144; Cornelissen, 2019:10). Moreno (1934:11) writes that this approach “enquire[s] into the evolution and organization of groups and the position of individuals within them.” Similar to Euler’s Königsberg bridge puzzle, Moreno emphasizes structure over physical distance, where social influence would spread through the connections between the girls.

Moreno’s conception of social networks influencing individual behavior was not an isolated view, for throughout the twentieth century, many sociologists used a systems-theoretic perspective on societies. Emile Durkheim, for example, argued that societies are comparable to biological systems, consisting of interrelated components, and emphasize the structure of the system above the intrinsic properties of the components. Borgatti et al. (2009:892) write, however, that Moreno’s sociometry was a way of making this abstract social structure tangible.

Sociometry is widely regarded as a precursor to social network analysis (SNA), but Freeman (1996, 2004) notes that the roots of SNA are complex and can also be traced back to the works of e.g. Almack (1922), Wellman (1926), Chevaleva-Janovskaja (1927), Bott (1928), Hubbard (1929), and Hagman (1933). Schnelle (2018:49) in turn identifies George Simmel as the father of SNA.

The works of Kurt Lewin were another clear forerunner of SNA (Prell, 2011:24; Cornelissen, 2019:13). Lewin saw the social environment as a ‘field’ (like the later Bourdieu¹), which he defined as “the totality of coexisting facts which are conceived of as mutually interdependent” (Lewin, 1951:240). Lewin (1939:889) writes,

Whether or not a certain type of behavior occurs depends not on the presence or absence of one fact or of a number of facts as viewed in isolation but upon the constellation (structure and forces) of the specific field as a whole. The ‘meaning’ of the single fact depends upon its position in the field; or, to say the same in more dynamical terms, the different parts of a field are mutually interdependent.

As far as the social field is concerned, Lewin came to a similar conclusion as Euler had when he tried to solve the Königsberg bridge puzzle, “the social field is actually an empirical space, which is as ‘real’ as a physical one. Euclidean space generally is not suited for adequately representing the structure of a social field – for instance, the relative position of groups, or a social locomotion” (Lewin, 1939:891). Lewin also introduced the concept of the shortest path to sociology (Bavelas, 1948:17), which would become a key issue in later publications by Milgram (1967) and Watts and Strogatz (1998).

Another discipline that would eventually have an important influence on the emerging science of networks was anthropology. Borgatti et al. (2009:893) write,

… building on the insights of the anthropologist Levi-Strauss, scholars began to represent kinship systems as relational algebras that consisted of a small set of generating relations (such as “parent of” and “married to”) together with binary composition operations to construct derived relations such as “in-law” and “cousin.” It was soon discovered that the kinship systems of such peoples as the Arunda of Australia formed elegant mathematical structures that gave hope to the idea that deep lawlike regularities might underlie the apparent chaos of human social systems.

The anthropologist Alex Bavelas (1948) was a student of Kurt Lewin and was the first to introduce the concept of centrality in social networks (Cioffi-Revilla, 2017:143; Cornelissen, 2019:13). Although he intended to define what Freeman (1977) later formalized as betweenness centrality, the measure he developed was closer to closeness centrality, which does not measure control over information in a network such as betweenness centrality, but rather independence from control (Freeman, 1980:585, 593). Nevertheless, Freeman (1980:586) credits Bavelas for his “intuition” that contributed to the future important development of both betweenness and closeness centralities.

Kochen and Pool’s studies in the 1950s led them to define random graphs (Amaral and Ottino, 2004:152) and the identification of what is known today as the small world phenomenon (Watts and Strogatz, 1998; Watts, 2004). Borgatti et al. (2009:892) write that Kochen and Pool speculated on the basis of mathematical models that at least 50% of pairs in a population like the United States can be connected by chains with no more than two intermediaries. Although their work was not published until the late 1970s (e.g. (Pool and Kochen, 1978)), it was widely distributed in pre-print form and is considered a direct influence on Stanley Milgram’s “six degrees of separation” (1967) studies (Amaral and Ottino, 2004:152; Cioffi-Revilla, 2017:144; Cornelissen, 2019:14).

Like Kurt Lewin and Emile Durkheim, Siegfried Nadel (1957) did not see societies as monolithic entities, but rather as a “pattern or network (or ‘system’) of relationships obtaining between actors in their capacity of playing roles relative to one another” (Nadel, 1957:12). Nadel’s work was one of the earliest formal treatments of the subject of social networks and influenced the later work of Harrison White (Prell, 2011:34).

During the 1950s, Leo Katz worked on centrality measures and developed Katz centrality in 1953 (Katz, 1953), which is closely related to Eigenvector centrality (Bonacich, 1987) and PageRank (Brin and Page, 1998) (Cornelissen, 2019:13). Some of his other key publications include Menzel and Katz (1955) and Coleman, Katz and Menzel (1957).

The term social networks first came into use in 1954, when it was introduced by the American anthropologist, J.A. Barnes (Teige, 2013:141; Cioffi-Revilla, 2017:143). In the 1960s, the focus of network research shifted from anthropology to sociology (Borgatti et al., 2009:893). One of the leading theorists in social network analysis was Linton Freeman, who formalized betweenness, closeness, and degree centrality (see (Freeman, 1977, 1979, 1980). However, it was not until the late 1960s and 1970s that SNA developed into a separate field within sociology, particularly at Harvard, where Harrison White institutionalized SNA (Cioffi-Revilla, 2017:144; Schnelle, 2018:49). White worked with many other influential researchers, including Stanley Milgram, and Peter Bearman, Phillip Bonacich, Ronald Breiger, Kathleen Carley, Ivan Chase, Mark Granovetter and Barry Wellman were some of his students (Cornelissen, 2019:15). Granovetter (1973) studied the links that connect different groups in a network and suggested that ‘weak ties’ have a special importance in the dissemination of information in social networks. Borgatti et al. (2009:893) write that by the late 1980s, social network analysis had become an established field within the social sciences, with a professional organization, an annual conference, specialized software and a journal (Social networks).

By the end of the 1960s, Von Bertalanffy (1969:20) noted that computers had opened up new ways of conducting research. Network science also became intricately interwoven with computerised research (Boissevain, 1979:392; Scott, 1996:212; Cornelissen, 2019:18-20), and Scott (1996:211) argues that the development of network theory is directly related to developments in computer software. In 1971, the program SOCPAC I, written in Fortran IV, was introduced (Cioffi-Revilla, 2017:144; Cornelissen, 2019:18). By the end of the decade, Tichy, Tushman and Fombrun (1979:513) name software platforms such as DIP, SocPac, SOCK, COMPLT, BLOCKER and CONCOR, while Haythornthwaite (1996:331) mentions GRADAP, STRUCTURE, UCINET, NEGOPY and KRACKPLOT. Harrison White and Gregory Heil produced BLOCKER in 1971, Richard Alba and Myron Gutmann produced SOCK in 1972, and Alba also produced COMPLT (Cornelissen, 2019:18), while CONCOR was introduced in Breiger, Boorman and Arabie (1975). UCINET was released in the early 1980s by Linton Freeman (Cioffi-Revilla, 2017:145) and GRADAP was developed by Robert Mokken, Jac Anthonisse and Frans Stokman (Cornelissen, 2019:15). In later years, newer software platforms would be developed to deal with the large amounts of data available since the 1990s, as is discussed later in the current article.

The first paper on random graphs was by Solomonoff and Rappaport (1951), although this publication had a much smaller influence on graph theory than the later Erdös and Rényi (1959, 1960) (Cioffi-Revilla, 2017:143). Pál Erdös and Alfréd Rényi (1959, 1960) developed one of the most influential models in graph theory, and their model laid the foundation for network models that would later evolve into the small-world (Watts and Strogatz, 1998) and scale-free (Barabási and Albert, 1999) models. Cornelissen (2019:22) writes that “practically all reviews” in physics start with a reference to Erdös and Rényi.

The view of economic systems as networks also gained ground in the twentieth century. One specific avenue of research was the study of company director networks, with Jeidels (1905) being the earliest example (Takes and Heemskerk, 2016:3). The League of Nations published The Network of World Trade (1942) in 1942, in which international trade is described as, “much more than the exchange of goods between one country and another; it is an intricate network that cannot be rent without loss” (League of Nations, 1942:7). In the seventies, Snyder and Kick (1979) and Steiber (1979) studied global economic interactions between countries as a system or network, and this approach has increasingly gained ground since the 1990s, as is discussed later.

The development of network theory in the twentieth century took place in parallel with the development of methods in intelligence analysis. During World War II, the US and British intelligence communities developed traffic analysis (also known as communication link analysis or movement analysis) (Van Meter, 2002:67; Ressler, 2006:6). Ressler (2006:6) explains,

This technique consists of the study of the external characteristics of communication in order to get information about the organization of the communication system. It is not concerned with the content of phone calls, but is interested in who calls whom and the network members, messengers, and gatekeepers. Traffic analysis was used by the British MI5 internal security service to combat the IRA in the 1980s and 1990s and continues to be used across the world by law-enforcement agencies including the U.S. Defense Intelligence Agency (DIA) Office of National Drug Control Policy.

Schnelle (2018:49) emphasizes, however, the difference between SNA and communication link analysis. Whereas SNA provides an analytical opportunity to quantify and measure relationships between similar nodes (humans), link analysis provides only the opportunity to compare links between nodes of different types. Communication link analysis will, for example, investigate who dialed a telephone number or to whom money was transferred from a bank account, while SNA instead focuses on the links between persons. In other words, communication link analysis predominantly analyses multi-mode networks where several types of nodes are present, while SNA regularly (but not exclusively) provides single-mode network analysis where only one type of node is present.

In 1996, Alta Analytics developed Netmap, which was one of the first commercial software platforms to facilitate link analysis (Van Meter, 2002:70). Netmap was used by amongst others the US Defense Intelligence Agency (DIA) Office of National Drug Control Policy (Van Meter, 2002:70).

Another similar method in intelligence analysis, the Village Survey Method, was introduced in Thailand in the 1960s by an officer of the Central Intelligence Agency (CIA), Ralph McGehee. McGehee used the Village Survey Method to analyse family and community ties around the secret structure of the Communist Party’s local and regional membership (Van Meter, 2002:67; Ressler, 2006:6; Roberts and Everton, 2011:2).

SNA continued to develop in parallel with traffic analysis and the Village Survey Method in the 1960s and 1970s, but by the 1990s, SNA became increasingly intertwined with methods in the intelligence community. Already in 1991, Sparrow (1991) argued that SNA holds considerable value for intelligence analysis, and the attacks on the World Trade Center on September 11, 2001 led to greater interest in SNA from the intelligence community, as is discussed later in the current article.

Big data and the connected age

In the late 1990s, networks became a topic of interest for physicists. The first seminal publication was an article by Duncan Watts and Steven Strogatz (1998), published in Nature, which argued that the small-world architecture of networks – as proposed by Milgram (1967) and Pool and Kochen (1978) – is a universal feature of complex networks and not just of social networks. In other words, power supply networks, metabolic processes, neural networks and other types of complex networks are comparable to social networks in terms of the average number of links that had to be traversed to reach a node from any other node. Network analysis was now more than just SNA: it became a tool in the study of complexity in general. Latapy, Magnien and Del Vecchio (2008:33) refer to a “post-1998” network theory because of the great influence this publication had.

In 1999, Barabási and Albert (1999) published an article in Science, which showed that complex networks are scale-free networks where the distribution of edges follow the power law. Boccaletti et al. (2006:177) write that this article, together with Watts and Strogatz (1998), caused a “wave of activity” in the physics community, which directly led to the popularity of this approach (these two publications are also singled out in Cornelissen (2019:20-21)).

The entry of physicists into network theory in the late 1990s was accompanied by and a result of the information explosion and the era of big data. Since the late 1990s, significantly larger networks have been analysed with millions and even billions of links, representing a new approach in network theory. Earlier studies of social networks were limited to small networks, for example Zachary’s (1977) study of social interactions at a karate club, but the information explosion made it possible to analyse large social networks such as the international film actor network (Amaral et al., 2000; Newman, Strogatz and Watts, 2001; Jeong, 2003; Guillaume and Latapy, 2004, 2006; Latapy, Magnien and Vecchio, 2008; Nacher and Akutsu, 2011; Tumminello et al., 2011). In economics, large networks such as company director networks (Davis, Yoo and Baker, 2003; Conyon and Muldoon, 2006; Durbach, Katshunga and Parker, 2013; Heemskerk, 2013; Heemskerk, Daolio and Tomassini, 2013; Senekal and Stemmet, 2014, 2019; Drago et al., 2015; Friel et al., 2016; Takes and Heemskerk, 2016; Williams, Deodutt and Stainbank, 2016; Drago and Ricciuti, 2017; Raddant, Milaković and Birg, 2017; Guo and Lv, 2018) and networks of global trade (De Benedictis and Tajoli, 2011; Glattfelder, 2013; Akerman and Seim, 2014; Senekal, Stemmet and Stemmet, 2015a, 2015b; Senekal, 2017a, 2020) could now be studied. In linguistics, the structure of language itself could now be modelled as a network (Dorogovtsev and Mendes, 2001; Ferrer I Cancho and Solé, 2001; de Jesus Holanda et al., 2004; Kosmidis, Kalampokis and Argyrakis, 2006; Liang et al., 2009; Cong and Liu, 2014; Ke et al., 2014; Senekal and Kotzé, 2017), as could cultural networks (Gleiser and Danon, 2003; Senekal, 2015, 2017b; Fraiberger et al., 2018; Wang et al., 2019).

Of particular note is the studies by Christakis, Fowler and co-authors (Christakis and Fowler, 2007, 2008; Fowler and Christakis, 2008, 2011; Rosenquist et al., 2010; Rosenquist, Fowler and Christakis, 2011; McDermott, Fowler and Christakis, 2013; Shakya et al., 2016). Using a large dataset, Christakis, Fowler, and co-authors found that behavioral patterns such as obesity (Christakis and Fowler, 2007), depression (Rosenquist, Fowler and Christakis, 2011), divorce (McDermott, Fowler and Christakis, 2013), happiness (Fowler and Christakis, 2008), smoking cessation (Christakis and Fowler, 2008), alcohol abuse (Rosenquist et al., 2010), and violence in intimate relationships (Shakya et al., 2016) spread across social networks. Referring to Christakis and Fowler’s research, Papachristos and co-authors (Papachristos, Braga and Hureau, 2012; Papachristos and Wildeman, 2014; Papachristos et al., 2015; Papachristos, Wildeman and Roberto, 2015; Green, Horel and Papachristos, 2017) found that firearm-based violence also spreads across social networks. In Green, Horel and Papachristos (2017:327), for example, the authors suggest that firearm-based violence also spreads like obesity across social networks through personal contact, and compares the spread to viruses.

This shift to studying larger networks had several analytical implications, including analyzing the network structure rather than the single node’s position in the network, and placing restrictions on the visualization of networks because such large networks cannot always be visualized in a meaningful way. Software developments also facilitated the study of larger networks, such as Pajek (Batagelj and Mrvar, 1998), Cytoscape (Shannon et al., 2003), and Gephi (Bastian, Heymann and Jacomy, 2009). Przulj and colleagues (Przulj, Corneil and Jurisica, 2004; Przulj and Higham, 2006; Kuchaiev et al., 2011) also created GraphCrunch, which facilitates the comparison of different networks, focused on but not limited to biological networks.

Much research has also been focused on the development of faster algorithms such as those of Latapy (2008), Blondel et al. (2008), Cohen et al. (2014), Liu et al. (2017), and Paluch et al. (2018), because calculations now had to be done on a significantly larger scale. Cohen et al. (2014) for instance found that calculating closeness centrality on a network of 23 947 nodes and 28 854 edges would take 44 222 hours or just over 5 years to complete. When analysing large networks such as the world wide web, developing faster algorithms is essential, which also led to the introduction of machine learning, as is discussed later in this article.

Adapting to the ranking of pages on the world wide web, Google founders Brin and Page (1998) suggested PageRank, a measure with which to rank search results, but which also proved valuable in other networks where important nodes were to be identified, such as citation networks. While PageRank was developed for Google, Kleinberg (1999) developed Hyperlink-Induced Topic Search (HITS) for Teoma and Ask.com.

Algorithms for the visualisation of larger networks also progressed from the earlier work by e.g. Eades (1984), Barnes and Hut (1986), Kamada and Kawai (1989), and Fruchterman and Reingold (1991), to newer, faster algorithms such as those by Walshaw (2003), Martin et al. (2011), and Hu (2012), which are capable of handling large networks.

This revolution in network studies does not mean that smaller networks cannot still be analysed: the analysis of large networks yields a different kind of knowledge than the analysis of small networks and especially within the Humanities, smaller networks often offer more meaningful answers. For example, it does not necessarily convey meaning that the link distribution pattern in a network follows either a Poisson or power law distribution, but it may be important that one person has a more advantageous position than another in a small social network. In literary studies, for instance, character interactions have been studied in a variety of publications (Stiller, Nettle and Dunbar, 2003; Stiller and Hudson, 2005; Carolina Sparavigna, 2013; Mac Carron and Kenna, 2013a, 2013b; Senekal, 2019), and these usually involve smaller networks.

The intelligence community also embraced network analysis in the era of big data. Krebs’s (2001) network analysis of the 9/11 hijackers was one of the key publications to highlight the promise that network analysis holds for the intelligence community, which was followed by Kochade’s (2006) analysis of the Bali bombings of 2002. Burcher and Whelan (2018) show that a plethora of academic studies investigated so-called ‘dark networks’ (illegal networks), including Schwartz and Rouselle (2009), Bright et al. (2012), Décary-Hétu and Dupont (2012), Bouchard and Amirault (2013), and Morselli (2013). More recently, Bright et al. (2021) also indicate the growing popularity of social network analysis in the study of dark networks.

This interest in networks by the intelligence community was supported by extensive defense budgets, and the Defense Advanced Research Projects Agency (DARPA), US Army Research Labs, the US Office of Naval Research (ONR), the National Security Agency (NSA), the National Science Foundation (NSF) and the Department of Homeland Security (DHS), funded research related to social network analysis (Ressler, 2006:7). This funding enabled the development of increasingly sophisticated software, and a full integration of intelligence analysis methods with network theory. Numerous software platforms, including Pathfinder, Sentinel Visualizer, and i2 Analyst’s Notebook, were developed around this time, some with funding specifically allocated from the US defense budget (Senekal, 2014:89). By 2006, the US Army and Marine Corps Counterinsurgency Field Manual (Department of the Army and Department of the Navy, 2006:B-10-B-17) included a dedicated section dealing with SNA, where they write SNA is, “a tool for understanding the organizational dynamics of an insurgency and how best to attack or exploit it” (2006:B-10).

Recent developments in Artificial Intelligence (AI), which includes machine learning (ML), opened up new opportunities with which to study complex networks. Quesada et al. (2019:1) write, “Combining complex networks analysis methods with machine learning (ML) algorithms have become a very useful strategy for the study of complex systems in applied sciences.” For instance, they (2019:2) suggest,

… descriptors of complex networks at the local and global scales (degree distribution, average degree, diameter of the network, average shortest path, clustering coefficient, connectedness, node centrality, and node influence) can be used as input variables to train ML algorithms in order to predict the properties of these systems.

One application of machine learning at the global (macro level) scale is Attar and Aliakbary (2017), who use machine learning to classify networks based on their topology. At the local (node-level) scale, Wen et al. (2018), Grando, Granville and Lamb (2018), Zhao et al. (2020), Bucur (2020) and Fan et al. (2020) developed ways in which to use machine learning instead of classic centrality measures to identify important nodes in a large network. Grando, Granville and Lamb (2018) suggest that using machine learning to identify important nodes is a direct result of analysing larger networks, since “typical centrality measures algorithms do not scale up to graphs with billions of edges (such as large social networks and Web graphs).” While still early in its development, integrating AI with network science is a promising avenue for future research.

Conclusion

Network science developed over the past century (with roots dating to the 1700s) with the help of scholars in a variety of disciplines. Today, network science has found applications in most disciplines. The network science community is enthusiastic about network science’s ability to provide new answers to complex phenomena, and Barabási (2011) for instance believes that network science has come to dominate the study of complexity.

The recent development of integrating AI with network science not only testifies to the adaptability of network science in the contemporary scientific environment, but also creates promising avenues of future research. As ever larger datasets are gathered and analysed, AI is sure to develop into a productive partner of network science.

Bibliography

Akerman, A. and Seim, A. L. 2014. The global arms trade network 1950–2007, Journal of comparative economics, 42(3), pp. 535–551. doi: 10.1016/j.jce.2014.03.001.

Almack, J. C. 1922. The influence of intelligence on the selection of associates, School and Society, 6, pp. 529–530.

Amaral, L. A., Scala, A., Barthelemy, M., and Stanley, H.E. 2000. Classes of small-world networks., Proceedings of the National Academy of Sciences of the United States of America, 97(21), pp. 11149–11152. doi: 10.1073/pnas.200327197.

Amaral, L. A. N. and Ottino, J. M. 2004. Complex Networks: Augmenting the framework for the study of complex systems, The European Physical Journal B – Condensed Matter, 38(2), pp. 147–162. doi: 10.1140/epjb/e2004-00110-5.

Attar, N. and Aliakbary, S. 2017. Classification of complex networks based on similarity of topological network features., Chaos, 27(9), p. 091102. doi: 10.1063/1.4997921.

Barabási, A.-L. 2011. The network takeover, Nature physics, 8(1), pp. 14–16. doi: 10.1038/nphys2188.

Barabási, A.-L. 2013. Network science., Philosophical Transactions. Series A, Mathematical, Physical, and Engineering Sciences, 371(1987), p. 20120375. doi: 10.1098/rsta.2012.0375.

Barabási, A.-L. 2016. Network Science. Cambridge, United Kingdom: Cambridge University Press, p. 475.

Barabási, A. and Albert, R. 1999. Emergence of scaling in random networks, Science, 286(5439), pp. 509–512. doi: 10.1126/science.286.5439.509.

Barabási, A. 2002. Linked: The New Science Of Networks. Cambridge, Mass: Perseus Books Group.

Barnes, J. and Hut, P. 1986. A hierarchical O(N log N) force-calculation algorithm, Nature, 324(6096), pp. 446–449. doi: 10.1038/324446a0.

Bastian, M., Heymann, S. and Jacomy, M. 2009. Gephi: An open source software for exploring and manipulating networks, Proceedings of the International AAAI Conference on Web and Social Media, 3(1).

Batagelj, V. and Mrvar, A. 1998. Pajek – Program for Large Network Analysis, Connections, 21(2), pp. 47–57.

Bavelas, A. 1948. A mathematical model for group structures, Human Organization, 7(3), pp. 16–30. doi: 10.17730/humo.7.3.f4033344851gl053.

Blondel, V. D., Guillaume, J., Lambiotte, R., and Lefebvre, E. 2008. Fast unfolding of communities in large networks, Journal of Statistical Mechanics: Theory and Experiment, 2008(10), p. P10008. doi: 10.1088/1742-5468/2008/10/P10008.

Boccaletti, S., Latora, V., Chavez, M., and Hwang, D.-U. 2006. Complex networks: Structure and dynamics, Physics Reports, 424(4–5), pp. 175–308. doi: 10.1016/j.physrep.2005.10.009.

Boissevain, J. 1979. Network analysis: A reappraisal, Current anthropology, 20(2), pp. 392–394. doi: 10.1086/202277.

Bonacich, P. 1987. Power and Centrality: A Family of Measures, American Journal of Sociology, 92(5), p. 1170. doi: 10.1086/228631.

Borgatti, S. P., Mehra, A., Brass, D.J., and Labianca, G. 2009. Network analysis in the social sciences., Science, 323(5916), pp. 892–895. doi: 10.1126/science.1165821.

Bott, H. 1928. Observation of play activities in a nursery school, Genetic psychology monographs, 4, pp. 44–88.

Bouchard, M. and Amirault, J. 2013. Advances in research on illicit networks, Global Crime, 14(2–3), pp. 119–122. doi: 10.1080/17440572.2013.801316.

Breiger, R. L., Boorman, S. A. and Arabie, P. 1975. An algorithm for clustering relational data with applications to social network analysis and comparison with multidimensional scaling, Journal of mathematical psychology, 12(3), pp. 328–383. doi: 10.1016/0022-2496(75)90028-0.

Bright, D., Brewer, R. and Morselli, C. 2021. Using social network analysis to study crime: Navigating the challenges of criminal justice records, Social networks, 66, pp. 50–64. doi: 10.1016/j.socnet.2021.01.006.

Bright, D. A., Hughes, C. E. and Chalmers, J. 2012. Illuminating dark networks: a social network analysis of an Australian drug trafficking syndicate, Crime, law, and social change, 57(2), pp. 151–176. doi: 10.1007/s10611-011-9336-z.

Brin, S. and Page, L. 1998. The anatomy of a large-scale hypertextual Web search engine, Computer Networks and ISDN Systems, 30(1–7), pp. 107–117. doi: 10.1016/S0169-7552(98)00110-X.

Bucur, D. 2020. Top influencers can be identified universally by combining classical centralities., Scientific Reports, 10(1), p. 20550. doi: 10.1038/s41598-020-77536-7.

Burcher, M. and Whelan, C. 2018. Social network analysis as a tool for criminal intelligence: understanding its potential from the perspectives of intelligence analysts, Trends in Organized Crime, 21(3), pp. 278–294. doi: 10.1007/s12117-017-9313-8.

Carolina Sparavigna, A. 2013. On social networks in plays and novels, International journal of sciences, 1(10), pp. 20–25. doi: 10.18483/ijSci.312.

Chevaleva-Janovskaja, E. 1927. Groupements spontanés d’enfants à l’age préscolaire, Archiv es de Psychologie, 20, pp. 219–223.

Christakis, N. A. and Fowler, J. H. 2007. The spread of obesity in a large social network over 32 years., The New England Journal of Medicine, 357(4), pp. 370–379. doi: 10.1056/NEJMsa066082.

Christakis, N. A. and Fowler, J. H. 2008. The collective dynamics of smoking in a large social network., The New England Journal of Medicine, 358(21), pp. 2249–2258. doi: 10.1056/NEJMsa0706154.

Cioffi-Revilla, C. 2017. Social Networks, in Introduction to computational social science. Cham: Springer International Publishing (Texts in computer science), pp. 141–192. doi: 10.1007/978-3-319-50131-4_4.

Coffman, D. 2021. François Quesnay, Luigi Pasinetti and the historical contexts of economic theory, Structural Change and Economic Dynamics, 56, pp. 64–73. doi: 10.1016/j.strueco.2020.10.005.

Cohen, E., Delling, D., Pajor, T., and Werneck, R.F. 2014. Computing classic closeness centrality, at scale, in Proceedings of the second edition of the ACM conference on Online social networks – COSN ’14. the second edition of the ACM conference, New York, New York, USA: ACM Press, pp. 37–50. doi: 10.1145/2660460.2660465.

Coleman, J., Katz, E. and Menzel, H. 1957. The diffusion of an innovation among physicians, Sociometry, 20(4), p. 253. doi: 10.2307/2785979.

Cong, J. and Liu, H. 2014. Approaching human language with complex networks., Physics of life reviews, 11(4), pp. 598–618. doi: 10.1016/j.plrev.2014.04.004.

Conyon, M. J. and Muldoon, M. R. 2006. The small world of corporate boards, Journal of Business Finance & Accounting, 33(9–10), pp. 1321–1343. doi: 10.1111/j.1468-5957.2006.00634.x.

Cornelissen, L. A. 2019. Social Network Cognition: An Empirical Investigation Of Network Accuracy and Social Position. Doctoral dissertation. University of Stellenbosch.

Davis, G. F., Yoo, M. and Baker, W. E. 2003. The Small World of the American Corporate Elite, 1982-2001, Strategic Organization, 1(3), pp. 301–326. doi: 10.1177/14761270030013002.

De Benedictis, L. and Tajoli, L. 2011. The world trade network, The World Economy, 34(8), pp. 1417–1454. doi: 10.1111/j.1467-9701.2011.01360.x.

Décary-Hétu, D. and Dupont, B. 2012. The social network of hackers, Global Crime, 13(3), pp. 160–175. doi: 10.1080/17440572.2012.702523.

Department of the Army and Department of the Navy 2006. The U.S. Army and Marine Corps Counterinsurgency Field Manual. Washington: Department of the Army and Department of the Navy.

Dorogovtsev, S. N. and Mendes, J. F. 2001. Language as an evolving word web., Proceedings. Biological Sciences / the Royal Society, 268(1485), pp. 2603–2606. doi: 10.1098/rspb.2001.1824.

Drago, C. and Ricciuti, R. 2017. Communities detection as a tool to assess a reform of the Italian interlocking directorship network, Physica A: Statistical Mechanics and its Applications, 466, pp. 91–104. doi: 10.1016/j.physa.2016.08.029.

Drago, C. et al. 2015. Corporate governance reforms, interlocking directorship and company performance in Italy, International review of law and economics, 41, pp. 38–49. doi: 10.1016/j.irle.2014.09.003.

Durbach, I., Katshunga, D. and Parker, H. 2013. Community structure and centrality effects in the South African company network, South African Journal of Business Management, 44(2), pp. 35–43. doi: 10.4102/sajbm.v44i2.154.

Eades, P. 1984. a Heuristic for graph Drawing, Congressus Numerantium, 42, pp. 149–160.

Erdös, P. and Rényi, A. 1959. On random graphs, Publicationes Mathematicae, 6, pp. 290–297.

Erdös, P. and Rényi, A. 1960. On the evolution of random graphs, Publications of the Mathematical Institute of the Hungarian Academy of Sciences, 5, pp. 17–61.

Estrada, E. 2011. The Structure Of Complex Networks. New York: Oxford University Press.

Euler, L. 1736. Solutio problematis ad geometriam situs pertinentis, Commentarii academiae scientiarum Petropolitanae, 8, pp. 128–140.

Fan, C., Zeng, L., Sun, Y., and Liu, Y.Y. 2020. Finding key players in complex networks through deep reinforcement learning., Nature Machine Intelligence, 2(6), pp. 317–324. doi: 10.1038/s42256-020-0177-2.

Ferrer I Cancho, R. and Solé, R. V. 2001. The small world of human language., Proceedings. Biological Sciences / the Royal Society, 268(1482), pp. 2261–2265. doi: 10.1098/rspb.2001.1800.

Fowler, J. H. and Christakis, N. A. 2008. Dynamic spread of happiness in a large social network: longitudinal analysis over 20 years in the Framingham Heart Study., BMJ (Clinical Research Ed.), 337, p. a2338. doi: 10.1136/bmj.a2338.

Fowler, J. H. and Christakis, N. A. 2011. Connected: The surprising power of our social networks and how they shape our lives. New York: Little, Brown Spark, p. 368.

Fraiberger, S. P., Sinatra, R., Resch, M., Riedl, C., and Barabási, A.L. 2018. Quantifying reputation and success in art., Science, 362(6416), pp. 825–829. doi: 10.1126/science.aau7224.

Freeman, L. C. 1977. A Set of Measures of Centrality Based on Betweenness, Sociometry, 40(1), p. 35. doi: 10.2307/3033543.

Freeman, L. C. 1979. Centrality in social networks conceptual clarification, Social networks, 1(3), pp. 215–239.

Freeman, L. C. 1980. The gatekeeper, pair-dependency and structural centrality, Quality & quantity, 14(4). doi: 10.1007/BF00184720.

Freeman, L. C. 1996. Some Antecedents of Social Network Analysis, Connections, 19(1), pp. 39–42.

Freeman, L. C. 2004. The Development of Social Network Analysis: A Study in the Sociology of Science. Vancouver, BC: Empirical Press, p. 218.

Friel, N., Rastelli, R., Wyse, J., and Raftery, A.E. 2016. Interlocking directorates in Irish companies using a latent space model for bipartite networks., Proceedings of the National Academy of Sciences of the United States of America, 113(24), pp. 6629–6634. doi: 10.1073/pnas.1606295113.

Fruchterman, T. M. J. and Reingold, E. M. 1991. Graph drawing by force-directed placement, Software: Practice and Experience, 21(11), pp. 1129–1164. doi: 10.1002/spe.4380211102.

Glattfelder, J. B. 2013. Decoding Complexity. Berlin, Heidelberg: Springer Berlin Heidelberg (Springer Theses). doi: 10.1007/978-3-642-33424-5.

Gleiser, P. M. and Danon, L. 2003. Community structure in jazz, Advances in Complex Systems, 06(04), pp. 565–573. doi: 10.1142/S0219525903001067.

Grando, F., Granville, L. Z. and Lamb, L. C. 2018. Machine learning in network centrality measures, ACM Computing Surveys, 51(5), pp. 1–32. doi: 10.1145/3237192.

Granovetter, M. S. 1973. The Strength of Weak Ties, American Journal of Sociology, 78(6), pp. 1360–1380. doi: 10.1086/225469.

Green, B., Horel, T. and Papachristos, A. V. 2017. Modeling contagion through social networks to explain and predict gunshot violence in Chicago, 2006 to 2014., JAMA internal medicine, 177(3), pp. 326–333. doi: 10.1001/jamainternmed.2016.8245.

Guillaume, J.-L. and Latapy, M. 2004. Bipartite structure of all complex networks, Information processing letters, 90(5), pp. 215–221. doi: 10.1016/j.ipl.2004.03.007.

Guillaume, J.-L. and Latapy, M. 2006. Bipartite graphs as models of complex networks, Physica A: Statistical Mechanics and its Applications, 371(2), pp. 795–813. doi: 10.1016/j.physa.2006.04.047.

Guo, C. and Lv, P. 2018. Network position of independent director in cross-border mergers and acquisitions, International Journal of Emerging Markets, 13(1), pp. 118–135. doi: 10.1108/IJoEM-01-2017-0027.

Hagman, E. P. 1933. The companionships of preschool children, University of Iowa Studies in Child Welfare, 7, pp. 10–69.

Haythornthwaite, C. 1996. Social network analysis: An approach and technique for the study of information exchange, Library & information science research, 18(4), pp. 323–342. doi: 10.1016/S0740-8188(96)90003-1.

Heemskerk, E. M., Daolio, F. and Tomassini, M. 2013. The community structure of the European network of interlocking directorates 2005-2010., Plos One, 8(7), p. e68581. doi: 10.1371/journal.pone.0068581.

Heemskerk, E. M. 2013. The rise of the European corporate elite: evidence from the network of interlocking directorates in 2005 and 2010, Economy and society, 42(1), pp. 74–101. doi: 10.1080/03085147.2012.686720.

Hubbard, R. M. 1929. A method of studying spontaneous group formation, in Thomas, D. S. (ed.) Some New Techniques for Studying Social Behavior. New York: Teachers College, Columbia University, Child Development Monographs, pp. 76–85.

Hu, Y. 2012. Algorithms for visualizing large networks, in Naumann, U. and Schenk, O. (eds.) Combinatorial Scientific Computing. London: Chapman and Hall/CRC, pp. 525–549. doi: 10.1201/b11644-20.

Jeidels, O. 1905. Das Verhältnis der Deutschen Grossbanken zur Industrie mit besonderer Berücksichtigung der Eisenindustrie. Duncker and Humblot.

Jeong, H. 2003. Complex scale-free networks, Physica A: Statistical Mechanics and its Applications, 321(1–2), pp. 226–237. doi: 10.1016/S0378-4371(02)01774-0.

de Jesus Holanda, A., Torres Pisa, I., Kinouchi, O., Souto Martinez, A., and Eduardo Seron Ruiz, E. 2004. Thesaurus as a complex network, Physica A: Statistical Mechanics and its Applications, 344(3–4), pp. 530–536. doi: 10.1016/j.physa.2004.06.025.

Kamada, T. and Kawai, S. 1989. An algorithm for drawing general undirected graphs, Information processing letters, 31(1), pp. 7–15. doi: 10.1016/0020-0190(89)90102-6.

Katz, L. 1953. A new status index derived from sociometric analysis, Psychometrika, 18(1), pp. 39–43. doi: 10.1007/BF02289026.

Ke, X., Zeng, Y., Ma, Q., and Zhu, L. 2014. Complex dynamics of text analysis, Physica A: Statistical Mechanics and its Applications, 415, pp. 307–314. doi: 10.1016/j.physa.2014.08.022.

Kleinberg, J. M. 1999. Authoritative sources in a hyperlinked environment, Journal of the ACM, 46(5), pp. 604–632. doi: 10.1145/324133.324140.

Koschade, S. 2006. A social network analysis of jemaah islamiyah: the applications to counterterrorism and intelligence, Studies in Conflict & Terrorism, 29(6), pp. 559–575. doi: 10.1080/10576100600798418.

Kosmidis, K., Kalampokis, A. and Argyrakis, P. 2006. Statistical mechanical approach to human language, Physica A: Statistical Mechanics and its Applications, 366, pp. 495–502. doi: 10.1016/j.physa.2005.10.039.

Krebs, V. 2001. Mapping Networks of Terrorist Cells, Connections, 24(3), pp. 43–52.

Kuchaiev, O., Stevanović, A., Hayes, W., and Pržulj, N. 2011. GraphCrunch 2: Software tool for network modeling, alignment and clustering., BMC Bioinformatics, 12, p. 24. doi: 10.1186/1471-2105-12-24.

Latapy, M., Magnien, C. and Vecchio, N. D. 2008. Basic notions for the analysis of large two-mode networks, Social networks, 30(1), pp. 31–48. doi: 10.1016/j.socnet.2007.04.006.

Latapy, M. 2008. Main-memory triangle computations for very large (sparse (power-law)) graphs, Theoretical computer science, 407(1–3), pp. 458–473. doi: 10.1016/j.tcs.2008.07.017.

League of Nations 1942. The Network of World Trade. Princeton: Princeton University Press.

Lewin, K. 1939. Field theory and experiment in social psychology: concepts and methods, American Journal of Sociology, 44(6), pp. 868–896. doi: 10.1086/218177.

Lewin, K. 1951. Field theory in social science. New York: Harper.

Liang, W., Shi, Y., Tse, C.K., Liu, J., Wang, Y., and Cui, X. 2009. Comparison of co-occurrence networks of the Chinese and English languages, Physica A: Statistical Mechanics and its Applications, 388(23), pp. 4901–4909. doi: 10.1016/j.physa.2009.07.047.

Liu, D., Jing, Y., Zhao, J., Wang, W., and Song, G. 2017. A Fast and Efficient Algorithm for Mining Top-k Nodes in Complex Networks., Scientific Reports, 7, p. 43330. doi: 10.1038/srep43330.

Mac Carron, P. and Kenna, R. 2013a. Network analysis of the Íslendinga sögur – the Sagas of Icelanders, The European Physical Journal B, 86(10), p. 407. doi: 10.1140/epjb/e2013-40583-3.

Mac Carron, P. and Kenna, R. 2013b. Viking sagas: Six degrees of Icelandic separation Social networks from the Viking era, Significance, 10(6), pp. 12–17. doi: 10.1111/j.1740-9713.2013.00704.x.

Martin, S., Brown, W.M., Klavans, R., and Boyack, K.W. 2011. OpenOrd: an open-source toolbox for large graph layout, in Visualization and Data Analysis 2011. IS&T/SPIE Electronic Imaging, SPIE (SPIE Proceedings), p. 786806. doi: 10.1117/12.871402.

McDermott, R., Fowler, J. H. and Christakis, N. A. 2013. Breaking up is hard to do, unless everyone else is doing it too: social network effects on divorce in a longitudinal sample, Social Forces, 92(2), pp. 491–519. doi: 10.1093/sf/sot096.

Menzel, H. and Katz, E. 1955. Social relations and innovation in the medical profession: the epidemiology of a new drug, Public opinion quarterly, 19(4), p. 337. doi: 10.1086/266584.

Milgram, S. 1967. The small world problem, Psychology Today, 2, pp. 60–67.

Moreno, J. 1934. Who Shall Survive? Washington, DC: Nervous and Mental Disease Publishing Company.

Morselli, Carlo. 2013. Crime and Networks (Criminology and Justice Studies). 1st ed. New York: Routledge, p. 360.

Nacher, J. C. and Akutsu, T. 2011. On the degree distribution of projected networks mapped from bipartite networks, Physica A: Statistical Mechanics and its Applications, 390(23–24), pp. 4636–4651. doi: 10.1016/j.physa.2011.06.073.

Nadel, S. F. 1957. The Theory of Social Structure. Glencoe: Free Press.

Newman, M. E. J., Strogatz, S. H. and Watts, D. J. 2001. Random graphs with arbitrary degree distributions and their applications, Physical Review E, 64(2). doi: 10.1103/PhysRevE.64.026118.

Paluch, R., Lu, X., Suchecki, K., Szymański, B.K., and Hołyst, J.A. 2018. Fast and accurate detection of spread source in large complex networks., Scientific Reports, 8(1), p. 2508. doi: 10.1038/s41598-018-20546-3.

Papachristos, A. V., Braga, A. A. and Hureau, D. M. 2012. Social networks and the risk of gunshot injury., Journal of Urban Health, 89(6), pp. 992–1003. doi: 10.1007/s11524-012-9703-9.

Papachristos, A. V., Wildeman, C. and Roberto, E. 2015. Tragic, but not random: the social contagion of nonfatal gunshot injuries., Social Science & Medicine, 125, pp. 139–150. doi: 10.1016/j.socscimed.2014.01.056.

Papachristos, A. V. and Wildeman, C. 2014. Network exposure and homicide victimization in an African American community., American Journal of Public Health, 104(1), pp. 143–150. doi: 10.2105/AJPH.2013.301441.

Papachristos, A. V., Braga, A.A., Piza, E., and Grossman, L.S. 2015. The company you keep? The spillover effects of gang membership on individual gunshot victimization in a co-offending network, Criminology; an interdisciplinary journal, 53(4), pp. 624–649. doi: 10.1111/1745-9125.12091.

Pool, I. de S. and Kochen, M. 1978. Contacts and influence, Social networks, 1(1), pp. 5–51. doi: 10.1016/0378-8733(78)90011-4.

Prell, C. 2011. Social Network Analysis: History, Theory and Methodology. Los Angeles: SAGE Publications Ltd.

Przulj, N., Corneil, D. G. and Jurisica, I. 2004. Modeling interactome: scale-free or geometric?, Bioinformatics, 20(18), pp. 3508–3515. doi: 10.1093/bioinformatics/bth436.

Przulj, N. and Higham, D. J. 2006. Modelling protein-protein interaction networks via a stickiness index., Journal of the Royal Society, Interface, 3(10), pp. 711–716. doi: 10.1098/rsif.2006.0147.

Quesada, D., Cruz-Monteagudo, M., Fletcher, T., Duardo-Sanchez, A., and González-Díaz, H. 2019. Complex networks and machine learning: from molecular to social sciences, Applied Sciences, 9(21), p. 4493. doi: 10.3390/app9214493.

Quesnay, F. 1758. Tableau Economique. No publication details available.

Raddant, M., Milaković, M. and Birg, L. 2017. Persistence in corporate networks, Journal of Economic Interaction and Coordination, 12(2), pp. 249–276. doi: 10.1007/s11403-015-0165-5.

Ressler, S. J. 2006. Social Network Analysis as an Approach to Combat Terrorism: Past, Present, and Future Research, Homeland Security Affairs, 2.

Roberts, N. and Everton, S. F. 2011. Strategies for combating dark networks, Journal of Social Structure, 12(1), pp. 1–32. doi: 10.21307/joss-2019-030.

Rosenquist, J. N., Fowler, J. H. and Christakis, N. A. 2011. Social network determinants of depression., Molecular Psychiatry, 16(3), pp. 273–281. doi: 10.1038/mp.2010.13.

Rosenquist, J. N., Murabito, J., Fowler, J.H., and Christakis, N.A. 2010. The spread of alcohol consumption behavior in a large social network., Annals of Internal Medicine, 152(7), pp. 426–33, W141. doi: 10.7326/0003-4819-152-7-201004060-00007.

Schnelle, S. 2018. Kartlegging av maritime hybride trusler. Kan bruk av stordata og sosial nettverksanalyse bidra til økt maritim situasjonsbevissthet? Master thesis. Forsvarets høgskole.

Schwartz, D. M. and (D.A.) Rouselle, T. 2009. Using social network analysis to target criminal networks, Trends in Organized Crime, 12(2), pp. 188–207. doi: 10.1007/s12117-008-9046-9.

Scott, J. 1996. Software review : A toolkit for social network analysis, Acta sociologica, 39(2), pp. 211–216. doi: 10.1177/000169939603900205.

Senekal, B. A. and Kotzé, E. 2017. Die statistiese eienskappe van geskrewe Afrikaans as ’n komplekse netwerk, LitNet Akademies Geesteswetenskappe, 14(1), pp. 27–59.

Senekal, B. A., Stemmet, J.-A. and Stemmet, K. 2015a. South Africa in the international Arms Trade Network (ATN) during ANC rule (1994-present): A network analysis, Journal for Contemporary History, 40(1), pp. 23–40.

Senekal, B. A., Stemmet, J.-A. and Stemmet, K. 2015b. South Africa in the international Arms Trade Network (ATN) during National Party rule (1948-1994): A network analysis, Journal for Contemporary History, 40(2), pp. 48–70.

Senekal, B. A. and Stemmet, K. 2014. The South African banking director network: an investigation into interlocking directorships using social network analysis (SNA), International Business & Economics Research Journal (IBER), 13(5), p. 963. doi: 10.19030/iber.v13i5.8765.

Senekal, B. A. and Stemmet, K. 2019. A weighted director network analysis of the big four banks on the Johannesburg Stock Exchange, South African Journal of Business Management, 50(1). doi: 10.4102/sajbm.v50i1.386.

Senekal, B. A. 2014. Mapping a dark network with Social Network Analysis (SNA): The Right-Wing Vaal Dam bomb plot, Journal for Contemporary History, 39(1), pp. 95–114.

Senekal, B. A. 2015. ʼn Kwantifisering van kleinwêreldheid in Afrikaanse kulturele netwerke in vergelyking met ander komplekse netwerke, LitNet Akademies Natuurwetenskappe, 12(3), pp. 665–688.

Senekal, B. A. 2017a. ’n Verkenning van Suid-Afrika se veranderende posisie in die wêreldhandelnetwerk 1948–1994, The Journal for Transdisciplinary Research in Southern Africa, 13(1), p. 10. doi: 10.4102/td.v13i1.430.

Senekal, B. A. 2017b. Die hedendaagse Afrikaanse poësiesisteem: Op soek na die mees verteenwoordigende wiskundige model van die rolspelerverhoudings daarbinne, Tydskrif vir Geesteswetenskappe, 57(1), pp. 168–186.

Senekal, B. A. 2019. ‘n Netwerkontleding van karakterinteraksies in N.P. van Wyk Louw se Germanicus, Stilet, 31(1), pp. 137–155.

Senekal, B. A. 2020. An alternative exploration of global political relations during the Cold War in Southern Africa: Modularity in the international Arms Trade Network (ATN) from 1975 to 1988, Politeia, 39(2), pp. 1–20.

Senekal, B. A. 2021a. Carrying Von Bertalanffy’s baton: Systems, complexity and network science, Part 1, Ensovoort, 42(5).

Senekal, B. A. 2021b. Carrying Von Bertalanffy’s baton: Systems, complexity and network science, Part 2, Ensovoort, 42(6).

Senekal, B. A. 2021c. Carrying Von Bertalanffy’s baton: Systems, complexity and network science, Part 3, Ensovoort, 42(6).

Shakya, H. B., Hughes, D.A., Stafford, D., Christakis, N.A., Fowler, J.H., and Silverman, J.G. 2016. Intimate partner violence norms cluster within households: an observational social network study in rural Honduras., BMC Public Health, 16(1), p. 233. doi: 10.1186/s12889-016-2893-4.

Shannon, P., Markiel, A., Ozier, O., Baliga, N.S., Wang, J.T., Ramage, D., Amin, N., Schwikowski, B., and Ideker, T. 2003. Cytoscape: a software environment for integrated models of biomolecular interaction networks., Genome Research, 13(11), pp. 2498–2504. doi: 10.1101/gr.1239303.

Snyder, D. and Kick, E. L. 1979. Structural Position in the World System and Economic Growth, 1955-1970: A Multiple-Network Analysis of Transnational Interactions, American Journal of Sociology, 84(5), pp. 1096–1126. doi: 10.1086/226902.

Solomonoff, R. and Rapoport, A. 1951. Connectivity of random nets, The Bulletin of mathematical biophysics, 13(2), pp. 107–117. doi: 10.1007/BF02478357.

Sparrow, M. K. 1991. The application of network analysis to criminal intelligence: An assessment of the prospects, Social networks, 13(3), pp. 251–274. doi: 10.1016/0378-8733(91)90008-H.

Steiber, S. R. 1979. The world system and world trade: an empirical exploration of conceptual conflicts, The Sociological quarterly, 20(1), pp. 23–36. doi: 10.1111/j.1533-8525.1979.tb02182.x.

Stiller, J. and Hudson, M. 2005. Weak links and scene cliques within the small world of Shakespeare, Journal of cultural and evolutionary psychology, 3(1), pp. 57–73. doi: 10.1556/JCEP.3.2005.1.4.

Stiller, J., Nettle, D. and Dunbar, R. I. M. 2003. The small world of Shakespeare’s plays., Human nature (Hawthorne, N.Y.), 14(4), pp. 397–408. doi: 10.1007/s12110-003-1013-1.

Takes, F. W. and Heemskerk, E. M. 2016. Centrality in the global network of corporate control, Social network analysis and mining, 6(1), p. 97. doi: 10.1007/s13278-016-0402-5.

Teige, O. 2013. Nettverk. En introduksjon til historisk nettverksanalyse, Norsk Slektshistorisk Tidsskrift, 43(3), pp. 139–157.

Tichy, N. M., Tushman, M. L. and Fombrun, C. 1979. Social network analysis for organizations, Academy of Management Review, 4(4), pp. 507–519. doi: 10.5465/amr.1979.4498309.

Tumminello, M., Miccichè, S., Lillo, F., Piilo, J., and Mantegna, R.N. 2011. Statistically validated networks in bipartite complex systems., Plos One, 6(3), p. e17994. doi: 10.1371/journal.pone.0017994.

Van Meter, K. M. 2002. Terrorists/Liberators: Researching and dealing with adversary social networks, Connections, 24(3), pp. 66–78.

Von Bertalanffy, L. 1969. General System Theory: Foundations, Development, Applications. New York: George Braziller Inc., p. 296.

Von Bertalanffy, L. 1972. The History and Status of General Systems Theory, Academy of Management Journal, 15(4), pp. 407–426. doi: 10.2307/255139.

Walshaw, C. 2003. A Multilevel Algorithm for Force-Directed Graph-Drawing, Journal of graph algorithms and applications, 7(3), pp. 253–285. doi: 10.7155/jgaa.00070.

Wang, X. et al. 2019. Success in books: predicting book sales before publication, EPJ Data Science, 8(1), p. 31. doi: 10.1140/epjds/s13688-019-0208-6.

Watts, D. J. and Strogatz, S. H. 1998. Collective dynamics of ‘small-world’ networks, Nature, 393(6684), pp. 440–442. doi: 10.1038/30918.

Watts, D. J. 2004. Six Degrees: The Science of a Connected Age. New York: W. W. Norton & Company, p. 384.

Wellman, B. 1926. The school child’s choice of companions, The Journal of educational research, 14, pp. 126–132.

Wen, X., Tu, C., Wu, M., and Jiang, X. 2018. Fast ranking nodes importance in complex networks based on LS-SVM method, Physica A: Statistical Mechanics and its Applications, 506, pp. 11–23. doi: 10.1016/j.physa.2018.03.076.

Williams, J. J., Deodutt, J. and Stainbank, L. J. 2016. An analysis of director interlocks on the Johannesburg Stock Exchange, South African Journal of Accounting Research, 30(2), pp. 120–138. doi: 10.1080/10291954.2015.1099203.

Zachary, W. W. 1977. An Information Flow Model for Conflict and Fission in Small Groups, Journal of anthropological research, 33(4), pp. 452–473. doi: 10.1086/jar.33.4.3629752.

Zhao, G., Jia, P., Huang, C., Zhou, A., and Fang, Y. 2020. A machine learning based framework for identifying influential nodes in complex networks, IEEE access : practical innovations, open solutions, 8, pp. 65462–65471. doi: 10.1109/ACCESS.2020.2984286.

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