Distributed Community Detection in Dynamic Graphs

Clementi, Andrea E. F.; Ianni, Miriam Di; Gambosi, Giorgio; Natale, Emanuele; Silvestri, Riccardo

doi:10.1007/978-3-319-03578-9_1

Cited by 10 publications

(17 citation statements)

References 35 publications

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“…Plurality consensus (a.k.a. majority consensus or proportionate agreement) is a fundamental problem arising in several areas such as distributed computing [3,16,30], communication networks [31], social networks [11,28,26] and biology [8].…”

Section: Md(c)mentioning

confidence: 99%

“…Further areas of interest of the multi-valued case include distributed cooperative decision-making and control in environmental monitoring, surveillance and security [32]. Finally, converging to the plurality color among a (large) set of initial node colors has been recently used as a basic building block for community detection in dynamic social networks [11]. We remark that, in all such applications, the data domain can span a relatively-large range of values, hence the importance of this problem for large values of k.…”

Section: Md(c)mentioning

confidence: 99%

See 1 more Smart Citation

Plurality Consensus in the Gossip Model

Becchetti¹,

Clementi²,

Natale³

et al. 2014

Proceedings of the Twenty-Sixth Annual ACM-SIAM Symposium on Discrete Algorithms

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We study Plurality Consensus in the GOSSIP Model over a network of n anonymous agents. Each agent supports an initial opinion or color. We assume that at the onset, the number of agents supporting the plurality color exceeds that of the agents supporting any other color by a sufficiently-large bias, though the initial plurality itself might be very far from absolute majority. The goal is to provide a protocol that, with high probability, brings the system into the configuration in which all agents support the (initial) plurality color.We consider the Undecided-State Dynamics, a wellknown protocol which uses just one more state (the undecided one) than those necessary to store colors.We show that the speed of convergence of this protocol depends on the initial color configuration as a whole, not just on the gap between the plurality and the second largest color community. This dependence is best captured by a novel notion we introduce, namely, the monochromatic distance md(c) which measures the distance of the initial color configurationc from the closest monochromatic one. In the complete graph, we prove that, for a wide range of the input parameters, this dynamics converges within O(md(c) log n) rounds. We prove that this upper bound is almost tight in the strong sense: Starting from any color configurationc, the convergence time is Ω(md(c)).Finally, we adapt the Undecided-State Dynamics to obtain a fast, random walk-based protocol for plurality consensus on regular expanders. This protocol converges in O(md(c) polylog(n)) rounds using only polylog(n) local memory. A key-ingredient to achieve the above bounds is a new analysis of the maximum node congestion that results from performing n parallel random walks on regular expanders.All our bounds hold with high probability.

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Section: Md(c)mentioning

confidence: 99%

Section: Md(c)mentioning

confidence: 99%

Plurality Consensus in the Gossip Model

Becchetti¹,

Clementi²,

Natale³

et al. 2014

Proceedings of the Twenty-Sixth Annual ACM-SIAM Symposium on Discrete Algorithms

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show abstract

“…However, in the proposed knowledge representation scheme, authors have only considered single relationship between actors. Clementi et al [17] proposed a distributed protocol based on the popular Label-Propagation approach. Lin et al [18] applied a novel integer programming (IP) approach to detect hierarchical community structures in social networks.…”

Section: Identification Technology For Community Structurementioning

confidence: 99%

Multi‐dimensional fuzzy trust evaluation for mobile social networks based on dynamic community structures

Chen

Wang

Yan

et al. 2016

Concurrency and Computation

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Summary As mobile social networks (MSNs) are booming and gaining tremendous popularity, there have been an increasing number of communications and interactions among users. Taking this advantage, users in MSNs make decisions via collecting and combining trust information from different users. Hence, trust evaluation technology has become a key requirement for network security in MSNs. In such MSNs, however, the community/group structures are dynamically changing, and users may belong to multiple communities/groups. Therefore, trust evaluation plays a critical role in inferring trustworthy contacts among users. In this paper, an innovative trust inference model is proposed for MSNs, in which multiple dimensional trust metrics are incorporated to reflect the complexity of trust. To infer trust relations between users in MSNs with complex communities, we first construct dynamic implicit social behavioral graphs (DynISBG) based on dynamic complex community/group structures and propose an efficient detection algorithm for DynISBG under fuzzy degree κ. We then present a multi‐dimensional fuzzy trust inferring approach that involves four metrics, that is, static attribute trust factor, dynamic behavioral trust factor, long‐term trust evolution factor, and recommendation‐based trust opinion. Moreover, to obtain the recommendation‐based trust opinion about indirect connected users, we discuss the trust aggregation and propagation along trust path. Finally, we evaluate the performance of our novel approach with simulations. The results show that, compared with the existing approaches, the proposed model provides a more detailed analysis in trust evaluation with higher accuracy. Copyright © 2016 John Wiley & Sons, Ltd.

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“…20,21 Most of CNA-based routing protocols use social graphs to generate the social metric for selecting the forwarding node. In Clementi et al, 22 examples of community-based detection algorithms are presented, in which the concepts of centrality 23 are introduced as a quantitative measure of the importance of a vertex in a graph and the concept of similarity 24 is used as a measurement of the degree of separation among groups of nodes. These social metrics are meant to positively impact the participation of a node in the network.…”

Section: B Social Network Analysismentioning

confidence: 99%

An altruism‐based trust‐dependent message forwarding protocol for opportunistic networks

Kumar

Dhurandher

Woungang

et al. 2016

Int J Communication

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Summary In opportunistic networks (OppNets), which are characterized by intermittent end‐to‐end connections, the messages are routed in a store‐carry‐and‐forward fashion using the locally inferred knowledge about the behavior of nodes. As such, most OppNets routing protocols use social metrics that are dependent on the nodes' past information. But the participation of nodes in the message forwarding process is not guaranteed without incentivizing them because most nodes are reluctant in sharing their private resources for public uses. In this paper, some socially derived psychological attributes of a node are introduced to ensure their trustworthy participation in the message forwarding process, leading to the design of an altruism‐dependent trust‐based data forwarding mechanism for OppNets (called ATDTN). In this protocol, each node is associated with a dynamically changing altruism value representing its trust in the network, which is used to determine its status with regard to its participation in message forwarding. Through trace‐driven simulations using the ONE simulator, it is shown that ATDTN outperforms IronMan and SimBet protocols for routing in OppNets (respectively, 18% and 48% improvement), in terms of delivery ratio, end‐to‐end delay, overhead count, and average number of hops, under varying buffer size and time‐to‐live.

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Distributed Community Detection in Dynamic Graphs

Cited by 10 publications

References 35 publications

Plurality Consensus in the Gossip Model

Plurality Consensus in the Gossip Model

Multi‐dimensional fuzzy trust evaluation for mobile social networks based on dynamic community structures

An altruism‐based trust‐dependent message forwarding protocol for opportunistic networks

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