Dynamic Competition Networks: Detecting Alliances and Leaders

Bonato, Anthony; Eikmeier, Nicole; Gleich, David F.; Malik, Rehan

doi:10.1007/978-3-319-92871-5_9

Cited by 9 publications

(15 citation statements)

References 21 publications

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“…Unlike the Sherlock Holmes novel studied in [KET + 19], we chose to focus on a television show, Veronica Mars. While there has been prior work on analyzing movies and television shows using graph theory [BDE + 16], [BEGM18], we believe that we are the first to apply knowledge graphs to television. Television has at least two distinct advantages over novels:…”

Section: A Knowledge Graphs For Fictionmentioning

confidence: 99%

Who killed Lilly Kane? A case study in applying knowledge graphs to crime fiction

Alaverdian¹,

Gilroy²,

Kirgios³

et al. 2020

Preprint

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We present a preliminary study of a knowledge graph created from season one of the television show Veronica Mars, which follows the eponymous young private investigator as she attempts to solve the murder of her best friend Lilly Kane. We discuss various techniques for mining the knowledge graph for clues and potential suspects. We also discuss best practice for collaboratively constructing knowledge graphs from television shows.

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Section: A Knowledge Graphs For Fictionmentioning

confidence: 99%

Who killed Lilly Kane? A case study in applying knowledge graphs to crime fiction

Alaverdian¹,

Gilroy²,

Kirgios³

et al. 2020

Preprint

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“…The Dynamic Competition Hypothesis (DCH) provides a quantitative framework for the structure of dynamic competition networks. We recall the statement of the DCH as first stated in [6]. Before we state the DCH, we present some terminology.…”

Section: The Dynamic Competition Hypothesismentioning

confidence: 99%

“…Members of an alliance are typically less likely to vote for each other, and this is the case in strong alliances. This is characterized in terms of near independent sets; see [6] for the formalism.…”

Section: The Dynamic Competition Hypothesismentioning

confidence: 99%

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Centrality in Dynamic Competition Networks

Bonato

Eikmeier

Gleich

et al. 2019

Complex Networks and Their Applications VIII

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Competition networks are formed via adversarial interactions between actors. The Dynamic Competition Hypothesis predicts that influential actors in competition networks should have a large number of common out-neighbors with many other nodes. We empirically study this idea as a centrality score and find the measure predictive of importance in several real-world networks including food webs, conflict networks, and voting data from Survivor.

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“…In social networks, negative correlation corresponds to enmity or rivalry between agents. We may also consider opposing networks formed by nation states or rival organizations, or alliances formed by mutually shared adversaries as in the game show Survivor [6,19].…”

Section: Introductionmentioning

confidence: 99%

The Iterated Local Model for Social Networks

Bonato¹,

Chuangpishit²,

English³

et al. 2019

Preprint

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On-line social networks, such as in Facebook and Twitter, are often studied from the perspective of friendship ties between agents in the network. Adversarial ties, however, also play an important role in the structure and function of social networks, but are often hidden. Underlying generative mechanisms of social networks are predicted by structural balance theory, which postulates that triads of agents, prefer to be transitive, where friends of friends are more likely friends, or anti-transitive, where adversaries of adversaries become friends. The previously proposed Iterated Local Transitivity (ILT) and Iterated Local Anti-Transitivity (ILAT) models incorporated transitivity and anti-transitivity, respectively, as evolutionary mechanisms. These models resulted in graphs with many observable properties of social networks, such as low diameter, high clustering, and densification.We propose a new, generative model, referred to as the Iterated Local Model (ILM) for social networks synthesizing both transitive and anti-transitive triads over time.In ILM, we are given a countably infinite binary sequence as input, and that sequence determines whether we apply a transitive or an anti-transitive step. The resulting model exhibits many properties of complex networks observed in the ILT and ILAT models. In particular, for any input binary sequence, we show that asymptotically the model generates finite graphs that densify, have clustering coefficient bounded away from 0, have diameter at most 3, and exhibit bad spectral expansion. We also give a thorough analysis of the chromatic number, domination number, Hamiltonicity, and isomorphism types of induced subgraphs of ILM graphs.

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Dynamic Competition Networks: Detecting Alliances and Leaders

Cited by 9 publications

References 21 publications

Who killed Lilly Kane? A case study in applying knowledge graphs to crime fiction

Who killed Lilly Kane? A case study in applying knowledge graphs to crime fiction

Centrality in Dynamic Competition Networks

The Iterated Local Model for Social Networks

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