Dynamic Community Mining and Tracking Based on Temporal Social Network Analysis

Zhou, Xiaokang; Liang, Wei; Wu, Bo; Lu, Zixian; Nishimura, Shoji; Saito, Takashi; Jin, Qun

doi:10.1109/cit.2016.74

Cited by 3 publications

(2 citation statements)

References 27 publications

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“…Social networks have been studied fairly extensively over the last couple of decades, mainly in the general context of analyzing interactions between people in order to determine important structural patterns in such interactions. With the utilization of plentiful data resources from online social media such as Facebook, Twitter, and Flickr, there's an increasing tendency in discovering community structures in such time-varying social networks (Alimadadi et al 2019;Rozario et al 2019;Zhou et al 2007Zhou et al , 2016. The emergence of online social networks has altered millions of web users' behavior so that their interactions with each other produce huge amounts of data on various activities.…”

Section: Social Network Analysismentioning

confidence: 99%

A survey of community detection methods in multilayer networks

Huang

Chen

Ren

et al. 2020

Data Min Knowl Disc

121

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Community detection is one of the most popular researches in a variety of complex systems, ranging from biology to sociology. In recent years, there’s an increasing focus on the rapid development of more complicated networks, namely multilayer networks. Communities in a single-layer network are groups of nodes that are more strongly connected among themselves than the others, while in multilayer networks, a group of well-connected nodes are shared in multiple layers. Most traditional algorithms can rarely perform well on a multilayer network without modifications. Thus, in this paper, we offer overall comparisons of existing works and analyze several representative algorithms, providing a comprehensive understanding of community detection methods in multilayer networks. The comparison results indicate that the promoting of algorithm efficiency and the extending for general multilayer networks are also expected in the forthcoming studies.

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Section: Social Network Analysismentioning

confidence: 99%

A survey of community detection methods in multilayer networks

Huang

Chen

Ren

et al. 2020

Data Min Knowl Disc

121

View full text Add to dashboard Cite

show abstract

“…In [85], Zhou et al proposed an optimal method in user networking model based on social analysis to improve information sharing in social computing environments. The proposed dynamic community detection framework includes a series of functional modules that can simultaneously extract the user's static and dynamic features and detect dynamic communities based on time trends.…”

Section: B Data Disseminationmentioning

confidence: 99%

Exploiting Mobile Social Networks From Temporal Perspective: A Survey

Zhou

Wang

et al. 2019

IEEE Access

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With the popularity of smart mobile devices, information exchange between users has become more and more frequent, and Mobile Social Networks (MSNs) have attracted significant attention in many research areas. Nowadays, discovering social relationships among people, as well as detecting the evolution of community have become hotly discussed topics in MSNs. One of the major features of MSNs is that the network topology changes over time. Therefore, it is not accurate to depict the social relationships of people based on a static network. In this paper, we present a survey of this emerging field from a temporal perspective. The state-of-the-art research of MSNs is reviewed with focus on four aspects: social property, time-varying graph, temporal social property, and temporal social properties-based applications. Some important open issues with respect to MSNs are discussed. INDEX TERMS Mobile social network, temporal perspective, temporal social properties, time-varying graph, temporal social properties-based applications.

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Routing Based on Community Division, Vitality, and Energy Balance in Delay Tolerant Networks

Liu

2019

Lecture Notes in Electrical Engineering

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Dynamic Community Mining and Tracking Based on Temporal Social Network Analysis

Cited by 3 publications

References 27 publications

A survey of community detection methods in multilayer networks

A survey of community detection methods in multilayer networks

Exploiting Mobile Social Networks From Temporal Perspective: A Survey

Routing Based on Community Division, Vitality, and Energy Balance in Delay Tolerant Networks

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