Overlapping Community Detection Based on Node Importance and Adjacency Information

Wang, Ping; Huang, Yonghong; Fei, Tang; Liu, Hongtao; Lü, Yangyang

doi:10.1155/2021/8690662

Cited by 63 publications

(4 citation statements)

References 47 publications

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“…Wang [21] designed node relevant centrality, is node important evaluation method to select core nodes. This node is selected as seed if score is greater than threshold according to node relevant centrality.…”

Section: Related Workmentioning

confidence: 99%

Exploring Overlapped Communities Based on Optimized Community’s Resolution Controlling Evaluation in Social Networks

2023

IJIES

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Community structure is one of the main structural features of networks and detecting overlapped community structure is an important field in social network analysis. In recent years, local community detection algorithms which detect overlapped community structure have been developed. However, the most existing algorithms suffer unstable community structure because the influences of parameter for controlling community's resolution of fitness evaluation functions where used in identifying communities. Therefore, this article designs the optimized parameter evaluation formula to avoid the parameter influences and the algorithm is modelled on local expansion strategy. In this work, firstly identifies the seed or core node by using extended jaccard similarity and form initial community via seed. Then local community is detected by expanding the initial community with fitness function based on proposed optimized parameter evaluation and finally overlapped nodes are identified by merging detected local communities. In this article, the algorithm is implemented by using small dataset from network data repository site and large networks from Stanford large network datasets collection. The performance results of algorithm are compared with LFM (Local Fitness Method), OSLOM (Order Statistics Local Optimization Method), DEMON (Democratic Estimate of Modular Organization of a Network), NILPA (Node Important based Label Propagation) and GREESE (Greedy Coupled-seeds Expansion) algorithm. Then, the proposed algorithm proves that it effectively performs and saves execution time.

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Section: Related Workmentioning

confidence: 99%

Exploring Overlapped Communities Based on Optimized Community’s Resolution Controlling Evaluation in Social Networks

2023

IJIES

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“…In addition, in order to determine the boundary nodes of the community, the node influence and similarity are integrated into local expansion. Wang et al (2021) proposed the ILE algorithm, which mainly determines the initial community by random walk and uses the adaptive function to expand the community, improving the effectiveness of community expansion.…”

Section: Related Workmentioning

confidence: 99%

An effective overlapping community merging method oriented to multidimensional attribute social networks

Cheng,

Yang,

Cheng

et al. 2023

Expert Systems

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For multidimensional attribute social networks, most of the existing methods have limitations in dealing with community redundancy, namely, the influences of the network's multidimensional attributes and users' interest preferences are ignored in the process of community merging. To this end, we propose an effective overlapping community merging method (EOCMM) oriented to multidimensional attribute social networks. In EOCMM, we focus on two core problems which are how to improve the method of improving overlapping community detection in multidimensional attribute social networks and how to merge the detected communities with high redundancy degree. To solve the first problem, based on the network topology characteristics and user interest preference, the Node Domain index is proposed to improve and optimize the selection of seed nodes. The traditional overlapping community detection method is improved by semantic fitness function and seed nodes. To solve the second problem, we merge the communities with high redundancy degree by Semantic Community Overlapping Degree which is fused by user similarity, community similarity and community tightness. Finally, we compared our method with other multiple mainstream methods by extensive experiments on three real social network datasets. The experimental comparison results show that the improvements on three evaluation metrics extension of modularity, partition density and semantic extension of modularity are 6.04%, 7.96% and 3.13%, respectively. And our method can make the results of community detection and merging more reasonable and effective.

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“…Many studies have been done based on topological networks (e.g., Raghavan et al, 2007;Wang et al, 2021). More straightforward approaches are often ineffective, inefficient and require more processing time.…”

Section: Introductionmentioning

confidence: 99%

Evaluation of Community Detection by Improving Influence Nodes in Complex Networks Using InfoMap with Sigmoid Fish Swarm Optimization Algorithm

Selvaraj¹,

Murugasamy²

2022

AIP

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In recent years, community detection is important because members of the same community share the same concepts. For efficient community detection in a social network, the influence node plays a vital role. A node in the social network or a user that has great influence and power would have a close relationship with a core of the group, termed a community. Therefore, the status of a person is determined by the user's influence strength. That is, a user who has greater influence and strength plays a vital role in the social media community and also acts as a core in the community of the social network. Therefore, a community is a group of nodes in the complex network structure which are interlinked with one another. Effective community detection in a complex structure is a challenging task. Many studies have been done based on topological networks. The approaches are ineffective, inefficient and require more time to process. To overcome these issues, this paper proposes improving the influence nodes in complex networks by using the InfoMap with sigmoid fish swarm optimization algorithm (I-SFSO). Our proposed I-SFSO gives better accuracy rates for the data sets: 92% for Dolphin, 95% for the Facebook dataset, 96% for the Twitter data set, 94% for the YouTube data set, 93% for a karate club and 94% for football.

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Overlapping Community Detection Based on Node Importance and Adjacency Information

Cited by 63 publications

References 47 publications

Exploring Overlapped Communities Based on Optimized Community’s Resolution Controlling Evaluation in Social Networks

Exploring Overlapped Communities Based on Optimized Community’s Resolution Controlling Evaluation in Social Networks

An effective overlapping community merging method oriented to multidimensional attribute social networks

Evaluation of Community Detection by Improving Influence Nodes in Complex Networks Using InfoMap with Sigmoid Fish Swarm Optimization Algorithm

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