Noise-tolerance community detection and evolution in dynamic social networks

Wang, Li; Jiang, Wang; Bi, Yuanjun; Wu, Weili; Xu, Wen; Lian, Biao

doi:10.1007/s10878-014-9719-z

Cited by 13 publications

(6 citation statements)

References 26 publications

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“…Some work has focused on detecting dynamic communities in evolving networks [16][17][18][19]. Recently, [20][21][22] attempted to discover communities from incomplete/noisy networks. All these methods assume that the entire network structure is available a priori, whereas we assume that only target nodes are given, and one needs to scan the nodes to explore the network structure.…”

Section: Related Workmentioning

confidence: 99%

Incremental community discovery via latent network representation and probabilistic inference

2019

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Most of the community detection algorithms assume that the complete network structure G = (V, E) is available in advance for analysis. However, in reality this may not be true due to several reasons, such as privacy constraints and restricted access, which result in a partial snapshot of the entire network. In addition, we may be interested in identifying the community information of only a selected subset of nodes (denoted by V T ⊆ V), rather than obtaining the community structure of all the nodes in G. To this end, we propose an incremental community detection method that repeats two stages-(i) network scan and (ii) community update. In the first stage, our method selects an appropriate node in such a way that the discovery of its local neighborhood structure leads to an accurate community detection in the second stage. We propose a novel criterion, called Information Gain, based on existing network embedding algorithms (Deepwalk and node2vec) to scan a node. The proposed community update stage consists of expectation-maximization and Markov Random Field-based denoising strategy. Experiments with 5 diverse networks with known ground-truth community structure show that our algorithm achieves 10.2% higher accuracy on average over state-of-the-art algorithms for both network scan and community update steps.

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Section: Related Workmentioning

confidence: 99%

Incremental community discovery via latent network representation and probabilistic inference

2019

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“…A nonlinear programming model was established in [21], which also adopted the Lagrangian method to reduce time complexity of computing the model. In [40], a novel algorithm was proposed to detect dynamic communities in social network, such that the effect of noisy data was eliminated, and the real community structure and abnormal events were discovered. In [46], a method with supervised learning mechanism was proposed to incorporate prior information into community structure detection.…”

Section: Detection Of Community Structuresmentioning

confidence: 99%

An integer programming approach and visual analysis for detecting hierarchical community structures in social networks

Lin

Kang

Chen

2015

Information Sciences

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“…Considering the online social network has its own propagation characteristics [14,15] , it includes positive and negative information in the dissemination of information. Positive information will be accepted and spread after lots of people continuously received many time of it.…”

Section: Verification Experimentsmentioning

confidence: 99%

Social Network Rumors Spread Model Based on Cellular Automata

Wang

Chen

2014

2014 10th International Conference on Mobile Ad-Hoc and Sensor Networks

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Describing the behavior of information dissemination in online social networks is propitious to understand the transmission process of real users in the online social networking sites. In this paper, we proposed a cellular automaton model to deal with the propagation characteristics of online social networks rumors spread. Experimental simulation was carried out under periodic boundary constraints in the process of rumor spread. The result showed that the cellular automaton model is indeed able to characterize the propagation behavior on online social networks. We also prescribed the immunization strategy to suppress the rumor spreading.

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Noise-tolerance community detection and evolution in dynamic social networks

Cited by 13 publications

References 26 publications

Incremental community discovery via latent network representation and probabilistic inference

Incremental community discovery via latent network representation and probabilistic inference

An integer programming approach and visual analysis for detecting hierarchical community structures in social networks

Social Network Rumors Spread Model Based on Cellular Automata

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