Subgraph centrality and clustering in complex hyper-networks

Estrada, Ernesto; Rodríguez-Velázquez, Juan A.

doi:10.1016/j.physa.2005.12.002

Cited by 264 publications

(167 citation statements)

References 45 publications

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“…So for such a complicated system we represented it using hypergraph notation because simple and bipartite or n-partite graph would not be sufficient to represent multidimensional and supra-dyadic relations that this system has (Estrada & Rodriguez-Velazquez, 2006) (Bonacich et al, 2004).…”

Section: The Case Of Dcfm-successmentioning

confidence: 99%

Developing a Conceptual Framework for Modeling Deviant Cyber Flash Mob: A Socio-Computational Approach Leveraging Hypergraph Constructs

Al-khateeb¹,

Agarwal²

2014

JDFSL

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In a Flash Mob (FM) a group of people get together in the physical world perform an unpredicted act and disperse quickly. Cyber Flash Mob (CFM) is the cyber manifestation of flash mob coordinated primarily using social media. Deviant Cyber Flash Mob (or, DCFM) is a special case of CFM, which is categorized as the new face of transnational crime organizations (TCOs). The DCFM phenomenon can be considered as a form of a cyber-collective action that is defined as an action aiming to improve group's conditions (such as, status or power). In this paper, we conduct a conceptual analysis of the DCFMs and model the factors that lead to success or failure with groundings in collective action and collective identity formation theories. Mathematical constructs of hypergraph are leveraged to represent the complex relations observed in the DCFM social networks. The model's efficacy is demonstrated through a test scenario.

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Section: The Case Of Dcfm-successmentioning

confidence: 99%

Developing a Conceptual Framework for Modeling Deviant Cyber Flash Mob: A Socio-Computational Approach Leveraging Hypergraph Constructs

Al-khateeb¹,

Agarwal²

2014

JDFSL

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“…One caveat of such an approach, however, is the relative paucity of available hypernetwork measures with which to investigate these more complicated objects. Recently, a handful of studies have proposed definitions for the hypernetwork analogue of some of the most popular complex network measures, such as the degree-distribution (Latapy et al, 2008), clustering coefficient (Estrada and Rodriguez-Velazquez, 2006;Zhou and Nakhleh, 2011;Gallagher and Goldberg, 2013) and measures of centrality (Estrada and Rodriguez-Velazquez, 2006;Pearcy et al, 2014). Unfortunately, these measures are often accompanied by increased algorithmic complexities, or are not well-posed, in the sense that a variety of different author-dependent definitions exist.…”

Section: Introductionmentioning

confidence: 99%

Complexity and robustness in hypernetwork models of metabolism

Pearcy

Chuzhanova

Crofts

2016

Journal of Theoretical Biology

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Metabolic reaction data is commonly modelled using a complex network approach, whereby nodes represent the chemical species present within the organism of interest, and connections are formed between those nodes participating in the same chemical reaction. Unfortunately, such an approach provides an inadequate description of the metabolic process in general, as a typical chemical reaction will involve more than two nodes, thus risking oversimplification of the the system of interest in a potentially significant way. In this paper, we employ a complex hypernetwork formalism to investigate the robustness of bacterial metabolic hypernetworks by extending the concept of a percolation process to hypernetworks. Importantly, this provides a novel method for determining the robustness of these systems and thus for quantifying their resilience to random attacks/errors. Moreover, we performed a site percolation analysis on a large cohort of bacterial metabolic networks and found that hypernetworks that evolved in more variable environments displayed increased levels of robustness and topological complexity.

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“…The use of more general logical structure as hypergraphs [7] seems to be more appropriate in these situations. Few attempts have been made to utilize hypergraphs in modeling a social network [8], and, more specifically, a scientific community network [9]. However, in our opinion, weighted multi-hypergraphs are the appropriate structures to represent multiple and weighted relationships.…”

Section: Introduction and Backgroundsmentioning

confidence: 99%

Eigenvector Centrality Based on Shared Research Topics in a Scientific Community

Volpentesta¹,

Felicetti²

2010

IFIP Advances in Information and Communication Technology

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Abstract. In this paper we propose a weighted multi-hypergraph as logical structure to model relationships between researchers and interest groups that join them on the base of shared research topics in a given scientific community. The well known concept of eingenvector centrality for graphs is extended to weighted multi-hypergraphs and we present a model instantiation for centrality analysis in the Pro-VE scientific community.

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Subgraph centrality and clustering in complex hyper-networks

Cited by 264 publications

References 45 publications

Developing a Conceptual Framework for Modeling Deviant Cyber Flash Mob: A Socio-Computational Approach Leveraging Hypergraph Constructs

Developing a Conceptual Framework for Modeling Deviant Cyber Flash Mob: A Socio-Computational Approach Leveraging Hypergraph Constructs

Complexity and robustness in hypernetwork models of metabolism

Eigenvector Centrality Based on Shared Research Topics in a Scientific Community

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