Community Detection of Time-Varying Mobile Social Networks

Chan, Shu Yan; Hui, Pan; Xu, Kuang

doi:10.1007/978-3-642-02466-5_115

Cited by 34 publications

(15 citation statements)

References 9 publications

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“…After building the aggregated contact graph, different social metrics can be obtained. For example, Hui, et al [79]- [82] proposed serval community detection approaches (simple, k-clique, modularity, etc.) with great potential to detect both static and temporal communities.…”

Section: A Social Graph and Contact Graphmentioning

confidence: 99%

See 1 more Smart Citation

A Survey of Social-Based Routing in Delay Tolerant Networks: Positive and Negative Social Effects

Zhu

Shi

et al. 2013

IEEE Commun. Surv. Tutorials

272

150

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Delay tolerant networks (DTNs) may lack continuous network connectivity. Routing in DTNs is thus challenging since it must handle network partitioning, long delays, and dynamic topology in such networks. In recent years, social-based approaches, which attempt to exploit social behaviors of DTN nodes to make better routing decision, have drawn tremendous interests in DTN routing design. In this article, we summarize the social properties in DTNs, and provide a survey of recent social-based DTN routing approaches. To improve routing performance, these methods either take advantages of positive social characteristics such as community and friendship to assist packet forwarding or consider negative social characteristics such as selfishness. We conclude by discussing some open issues and challenges in socialbased approaches regarding the design of DTN routing protocols.

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Section: A Social Graph and Contact Graphmentioning

confidence: 99%

“…Therefore, the knowledge of community structures could help a routing protocol to choose better forwarding relays for particular destinations, and hence improve the chance of delivery. Many proposed community detection algorithms [50], [79]- [82] are available for identifying social communities from the contact graph of DTNs.…”

Section: B Communitymentioning

confidence: 99%

A Survey of Social-Based Routing in Delay Tolerant Networks: Positive and Negative Social Effects

Zhu

Shi

et al. 2013

IEEE Commun. Surv. Tutorials

272

150

View full text Add to dashboard Cite

show abstract

“…However, the community structure in [18] is detected based on the cumulative node contact characteristics, and is considered as fixed during the data forwarding process. Time-varying community structure is studied in [7], but is not specifically exploited for data forwarding. Comparatively, in this paper we exploit multiple perspectives of transient social contact patterns at the fine-grained scale to improve the effectiveness of data forwarding.…”

Section: Related Workmentioning

confidence: 99%

On Exploiting Transient Social Contact Patterns for Data Forwarding in Delay-Tolerant Networks

Gao

Porta

2013

IEEE Trans. on Mobile Comput.

106

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Abstract-Unpredictable node mobility, low node density, and lack of global information make it challenging to achieve effective data forwarding in Delay-Tolerant Networks (DTNs). Most of the current data forwarding schemes choose the nodes with the best cumulative capability of contacting others as relays to carry and forward data, but these nodes may not be the best relay choices within a short time period due to the heterogeneity of transient node contact characteristics. In this paper, we propose a novel approach to improve the performance of data forwarding with a short time constraint in DTNs by exploiting the transient social contact patterns. These patterns represent the transient characteristics of contact distribution, network connectivity and social community structure in DTNs, and we provide analytical formulations on these patterns based on experimental studies of realistic DTN traces. We then propose appropriate forwarding metrics based on these patterns to improve the effectiveness of data forwarding. When applied to various data forwarding strategies, our proposed forwarding metrics achieve much better performance compared to existing schemes with similar forwarding cost.

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“…Non-negative matrix factorization was introduced to evolution analysis [16] as well. Besides these algorithms concerned with the evolution procedures of communities, community detection in dynamic social networks aims to detect the optimal community partition at each timestamp [4,7,[17][18][19]. Moreover, in order to describe the change of communities at different timestamps, tracking algorithms [20,21] based on similarity comparison have also been studied.…”

Section: Open Accessmentioning

confidence: 99%

Core-Based Dynamic Community Detection in Mobile Social Networks

Wang

et al. 2013

Entropy

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Abstract:The topic of community detection in social networks has attracted a lot of attention in recent years. Existing methods always depict the relationship of two nodes using the snapshot of the network, but these snapshots cannot reveal the real relationships, especially when the connection history among nodes is considered. The problem of detecting the stable community in mobile social networks has been studied in this paper. Community cores are considered as stable subsets of the network in previous work. Based on these observations, this paper divides all nodes into a few of communities due to the community cores. Meanwhile, communities can be tracked through incremental computing. Experimental results based on real-world social networks demonstrate that our proposed method performs better than the well-known static community detection algorithm in mobile social networks.

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Community Detection of Time-Varying Mobile Social Networks

Cited by 34 publications

References 9 publications

A Survey of Social-Based Routing in Delay Tolerant Networks: Positive and Negative Social Effects

A Survey of Social-Based Routing in Delay Tolerant Networks: Positive and Negative Social Effects

On Exploiting Transient Social Contact Patterns for Data Forwarding in Delay-Tolerant Networks

Core-Based Dynamic Community Detection in Mobile Social Networks

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