Modeling community detection using slow mixing random walks

Torghabeh, Ramezan Paravi; Santhanam, Narayana

doi:10.1109/bigdata.2015.7364008

Cited by 5 publications

(1 citation statement)

References 29 publications

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“…These metrics can be, for example, modularity, and we can optimize them through a global optimization approach [32,33]. Local methods of community detection consider local information of the network and thus approaches using random walk processes, cliques, influential nodes or leaders work as local methods [34,35,36,37,38,39,40]. Furthermore, considering other sources of information may provide more realistic data to identify communities.…”

Section: Community Detectionmentioning

confidence: 99%

The Significant Effect of Overlapping Community Structures in Signed Social Networks

Shahriari

Liu

Klamma

2017

Lecture Notes in Social Networks

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Many real-world networks can be modeled by networks of interacting agents. Analysis of these interactions can reveal fundamental properties from these networks. Estimating the amount of collaboration in a network corresponding to connections in a learning environment can reveal to what extent learners share their experience and knowledge with other learners. Alternatively, analyzing the network of interactions in an open source software project can manifest indicators showing the efficiency of collaborations. One central problem in such domains is the low cooperativity values of networks due to the low cooperativity values of their respective communities. So administrators should not only understand and predict the cooperativity of networks but also they need to evaluate their respective community structures. To approach this issue, in this paper, we address two domains of open source software projects and learning forums. As such, we calculate the amount of cooperativity in the corresponding networks and communities of these domains by applying several community detection algorithms. Moreover, we investigated the community properties and identified the significant properties for estimating the network and community cooperativity. Correspondingly, we identified to what extent various community detection algorithms affect the identification of significant properties and prediction of cooperativity. We also fabricated binary and regression prediction models using the community properties. Our results and constructed models can be used to infer cooperativity of community structures from their respective properties. When predicting high defective structures in networks, administrators can look for useful drives to increase the collaborations.

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Section: Community Detectionmentioning

confidence: 99%

The Significant Effect of Overlapping Community Structures in Signed Social Networks

Shahriari

Liu

Klamma

2017

Lecture Notes in Social Networks

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A Supervised Learning Community Detection Method Based on Attachment Graph Model

Zhao

Yan

Zhao³

2022

Advanced Information Systems Engineering

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Identifying vital nodes for influence maximization in attributed networks

Wang

Zheng

Liu

2022

Sci Rep

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Identifying a set of vital nodes to achieve influence maximization is a topic of general interest in network science. Many algorithms have been proposed to solve the influence maximization problem in complex networks. Most of them just use topology information of networks to measure the node influence. However, the node attribute is also an important factor for measuring node influence in attributed networks. To tackle this problem, we first propose an extension model of linear threshold (LT) propagation model to simulate the information propagation in attributed networks. Then, we propose a novel community-based method to identify a set of vital nodes for influence maximization in attributed networks. The proposed method considers both topology influence and attribute influence of nodes, which is more suitable for identifying vital nodes in attributed networks. A series of experiments are carried out on five real world networks and a large scale synthetic network. Compared with CELF, IMM, CoFIM, HGD, NCVoteRank and K-Shell methods, experimental results based on different propagation models show that the proposed method improves the influence spread by $$-2.28\% \, \textrm{to} \, 4.76\%$$ - 2.28 % to 4.76 % , $$-2.50\% \, \textrm{to} \, 16.97\%$$ - 2.50 % to 16.97 % , $$0.18\% \, \textrm{to} \, 16.07\%$$ 0.18 % to 16.07 % , $$0.22\% \, \textrm{to} \, 41.82\%$$ 0.22 % to 41.82 % , $$0.23\% \, \textrm{to} \, 11.24\%$$ 0.23 % to 11.24 % and $$10.78\% \, \textrm{to} \, 75.22\%$$ 10.78 % to 75.22 % .

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Modeling community detection using slow mixing random walks

Cited by 5 publications

References 29 publications

The Significant Effect of Overlapping Community Structures in Signed Social Networks

The Significant Effect of Overlapping Community Structures in Signed Social Networks

A Supervised Learning Community Detection Method Based on Attachment Graph Model

Identifying vital nodes for influence maximization in attributed networks

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