Finding Cliques in Social Networks: A New Distribution-Free Model

Fox, Jacob; Roughgarden, Tim; Seshadhri, C.; Fan, Wei; Wein, Nicole

doi:10.1137/18m1210459

Cited by 35 publications

(65 citation statements)

References 63 publications

(69 reference statements)

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“…rumour spread, marketing, infection spread, etc.). While it is interesting to analyse the model on arbitrary random networks, it is of particular interest to consider random networks with plausible sizes and densities that accurately reproduce society (see [16,[25][26][27][28]).…”

Section: Discussionmentioning

confidence: 99%

A trust model for spreading gossip in social networks: a multi-type bootstrap percolation model

Bhansali¹,

Schaposnik

2020

Proc. R. Soc. A.

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We introduce here a multi-type bootstrap percolation model, which we call T -Bootstrap Percolation (T -BP), and apply it to study information propagation in social networks. In this model, a social network is represented by a graph G whose vertices have different labels corresponding to the type of role the person plays in the network (e.g. a student, an educator, etc.). Once an initial set of vertices of G is randomly selected to be carrying a gossip (e.g. to be infected), the gossip propagates to a new vertex provided it is transmitted by a minimum threshold of vertices with different labels. By considering random graphs, which have been shown to closely represent social networks, we study different properties of the T -BP model through numerical simulations, and describe its implications when applied to rumour spread, fake news, and marketing strategies.

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Section: Discussionmentioning

confidence: 99%

A trust model for spreading gossip in social networks: a multi-type bootstrap percolation model

Bhansali¹,

Schaposnik

2020

Proc. R. Soc. A.

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“…Third, A may be incentivized to encourage the B-C friendship if maintaining a single triadic friendship is viewed as more efficient than maintaining a separate pair of dyadic friendships with B and C separately. Building on these ideas it is also natural, as in [11,20], to argue that the increased likelihood that B and C will become connected via triadic closure grows in proportion to the number of friends they have in common.…”

Section: Motivation and Backgroundmentioning

confidence: 99%

A Hierarchy of Network Models Giving Bistability Under Triadic Closure

Giovacchino¹,

Higham²,

Zygalakis³

2021

Preprint

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Triadic closure describes the tendency for new friendships to form between individuals who already have friends in common. It has been argued heuristically that the triadic closure effect can lead to bistability in the formation of large-scale social interaction networks. Here, depending on the initial state and the transient dynamics, the system may evolve towards either of two long-time states. In this work, we propose and study a hierarchy of network evolution models that incorporate triadic closure, building on the work of Grindrod, Higham and Parsons [Internet Mathematics, 8, 2012, 402-423]. We use a chemical kinetics framework, paying careful attention to the reaction rate scaling with respect to the system size. In a macroscale regime, we show rigorously that a bimodal steady state distribution is admitted. This behavior corresponds to the existence of two distinct stable fixed points in a deterministic mean-field ODE. The macroscale model is also seen to capture an apparent metastability property of the microscale system. Computational simulations are used to support the analysis.

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“…Because graphs are non-linear data structures [20], [21] they are a challenging and interesting research area that can be used to organize, simulate, model and solve a lot of real world problems [22], [23], [24] and thus it has become more popular both in scientific as well as commercial fields. Graph analysis [25] has been studied for a long time, and is the premise for many applications such as those associated with social networks, telephone networks, program flows, bio-informatics, chemical compounds, terrorist networks, etc., with closed frequent subgraph mining forming the fundamental basis for graph clustering, graph based anomaly detection, and graph classification [26], [27], [28].…”

Section: Introductionmentioning

confidence: 99%

A Method for Closed Frequent Subgraph Mining in a Single Large Graph

Nguyen

Zelinka

et al. 2021

IEEE Access

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Mining frequent subgraphs is an interesting and important problem in the graph mining field, in that mining frequent subgraphs from a single large graph has been strongly developed, and has recently attracted many researchers. Among them, MNI-based approaches are considered as state-of-the-art, such as the GraMi algorithm. Besides frequent subgraph mining (FSM), frequent closed frequent subgraph mining was also developed. This has many practical applications and is a fundamental premise for many studies. This paper proposes the CloGraMi (Closed Frequent Subgraph Mining) algorithm based on GraMi to find all closed frequent subgraphs in a single large graph. Two effective strategies are also developed, the first one is a new level order traversal strategy to quickly determine closed subgraphs in the searching process, and the second is setting a condition for early pruning a large portion of non-closed candidates, both of them aim to reduce the running time as well as the memory requirements, improve the performance of the proposed algorithm. Our experiments are performed on five real datasets (both directed and undirected graphs) and the results show that the running time as well as the memory requirements of our algorithm are significantly better than those of the GraMi-based algorithm.INDEX TERMS data mining; frequent closed subgraph; social network; pruning strategy.

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Finding Cliques in Social Networks: A New Distribution-Free Model

Cited by 35 publications

References 63 publications

A trust model for spreading gossip in social networks: a multi-type bootstrap percolation model

A trust model for spreading gossip in social networks: a multi-type bootstrap percolation model

A Hierarchy of Network Models Giving Bistability Under Triadic Closure

A Method for Closed Frequent Subgraph Mining in a Single Large Graph

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