A Study on Overlapping Community Detection for Multimedia Social Network

2019 ◽  
pp. 572-578
Author(s):  
Sabah Fatima ◽  
Srivinasu Badugu
Keyword(s):  
Social Network ◽  
Community Detection ◽  
Overlapping Community Detection ◽  
Overlapping Community ◽  
Multimedia Social Network

Aspects of Various Community Detection Algorithms in Social Network Analysis

2019 ◽  
pp. 327-341
Author(s):  
Nicole Belinda Dillen ◽  
Aruna Chakraborty
Keyword(s):  
Social Network ◽  
Social Network Analysis ◽  
Network Analysis ◽  
Community Detection ◽  
Overlapping Community Detection ◽  
Detection Algorithms ◽  
Novel Approach ◽  
Recent Developments ◽  
Overlapping Community ◽  
Related Individuals

One of the most important aspects of social network analysis is community detection, which is used to categorize related individuals in a social network into groups or communities. The approach is quite similar to graph partitioning, and in fact, most detection algorithms rely on concepts from graph theory and sociology. The aim of this chapter is to aid a novice in the field of community detection by providing a wider perspective on some of the different detection algorithms available, including the more recent developments in this field. Five popular algorithms have been studied and explained, and a recent novel approach that was proposed by the authors has also been included. The chapter concludes by highlighting areas suitable for further research, specifically targeting overlapping community detection algorithms.

scholarly journals Multilayer Social Network Overlapping Community Detection Algorithm Based on Trust Relationship

2021 ◽  
Vol 2021 ◽  
pp. 1-14
Author(s):  
Junjie Jia ◽  
Pengtao Liu ◽  
Xiaojin Du ◽  
Yuchao Zhang
Keyword(s):  
Social Network ◽  
Community Detection ◽  
Detection Algorithm ◽  
Experimental Comparison ◽  
Overlapping Community Detection ◽  
Trust Value ◽  
Overlapping Communities ◽  
Trust Relationship ◽  
Overlapping Community ◽  
Community Detection Algorithm

Aiming at the problem of the lack of user social attribute characteristics in the process of dividing overlapping communities in multilayer social networks, in this paper, we propose a multilayer social network overlapping community detection algorithm based on trust relationship. By combining structural trust and social attribute trust, we transform a complex multilayer social network into a single-layer trust network. We obtain the community structure according to the community discovery algorithm based on trust value and merge communities with higher overlap. The experimental comparison and analysis are carried out on the synthetic network and the real network, respectively. The experimental results show that the proposed algorithm has higher harmonic mean and modularity than other algorithms of the same type.

scholarly journals A Semantic Community Detection Algorithm Based on Quantizing Progress

Complexity ◽  
2019 ◽  
Vol 2019 ◽  
pp. 1-13 ◽  
Author(s):  
Xu Han ◽  
Deyun Chen ◽  
Hailu Yang
Keyword(s):  
Social Network ◽  
Community Detection ◽  
Semantic Information ◽  
Semantic Space ◽  
Detection Algorithm ◽  
Overlapping Community Detection ◽  
Detection Algorithms ◽  
Overlapping Community ◽  
Community Detection Algorithm ◽  
The Ideal

The semantic social network is a kind of network that contains enormous nodes and complex semantic information, and the traditional community detection algorithms could not give the ideal cogent communities instead. To solve the issue of detecting semantic social network, we present a clustering community detection algorithm based on the PSO-LDA model. As the semantic model is LDA model, we use the Gibbs sampling method that can make quantitative parameters map from semantic information to semantic space. Then, we present a PSO strategy with the semantic relation to solve the overlapping community detection. Finally, we establish semantic modularity (SimQ) for evaluating the detected semantic communities. The validity and feasibility of the PSO-LDA model and the semantic modularity are verified by experimental analysis.

scholarly journals Overlapping Community Detection Based on Node Importance and Adjacency Information

2021 ◽  
Vol 2021 ◽  
pp. 1-17
Author(s):  
Ping Wang ◽  
Yonghong Huang ◽  
Fei Tang ◽  
Hongtao Liu ◽  
Yangyang Lu
Keyword(s):  
Community Structure ◽  
Social Network ◽  
Community Detection ◽  
Evaluation Method ◽  
Detection Algorithm ◽  
Overlapping Community Detection ◽  
Overlapping Communities ◽  
Node Importance ◽  
Overlapping Community ◽  
Community Detection Algorithm

Detecting the community structure and predicting the change of community structure is an important research topic in social network research. Focusing on the importance of nodes and the importance of their neighbors and the adjacency information, this article proposes a new evaluation method of node importance. The proposed overlapping community detection algorithm (ILE) uses the random walk to select the initial community and adopts the adaptive function to expand the community. It finally optimizes the community to obtain the overlapping community. For the overlapping communities, this article analyzes the evolution of networks at different times according to the stability and differences of social networks. Seven common community evolution events are obtained. The experimental results show that our algorithm is feasible and capable of discovering overlapping communities in complex social network efficiently.

scholarly journals Parallel seed selection method for overlapping community detection in social network

2018 ◽  
Vol 19 (4) ◽  
pp. 375-385
Author(s):  
Belfin R V ◽  
Grace Mary Kanaga E
Keyword(s):  
Social Network ◽  
Community Detection ◽  
Research Problem ◽  
Seed Selection ◽  
Overlapping Community Detection ◽  
Modern Times ◽  
The Social ◽  
Current Scenario ◽  
Overlapping Community ◽  
Processing Engine

Social network analysis is one of the key areas of research during modern times. The social network is growing with more users and the ties between them day by day. This reason brings out many research queries and new conclusions from this area. Overlapping community detection in the social network is one such research problem which has acquired interest among researchers nowadays. Earlier, the investigation was in finding out algorithms to detect communities in the network sequentially. There are many distinguished findings toward overlapping community detection. Due to the velocity of data in the current era, the available algorithms will be a bit sluggish in processing the data. The proposed algorithm uses parallel processing engine to resolve this delay problem in the current scenario. The algorithm in parallel finds out the superior seed set in the network and expands it in parallel to find out the community. The work shows amazing improvement in the runtime and also detects quality groups in the network.

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