A novel two-step community detection approach based on community tree and the N-players cooperative game in large-scale social networks

2018 ◽  
Vol 18 (4) ◽  
pp. 1007-1020
Author(s):  
Decheng Zhang ◽  
Yakun Huang ◽  
Yang Wang ◽  
Yujun Zhu ◽  
Chuanxin Zhao
Keyword(s):  
Social Networks ◽  
Community Detection ◽  
Cooperative Game ◽  
Large Scale ◽  
Detection Approach

scholarly journals A Community-Based Approach for Link Prediction in Signed Social Networks

2015 ◽  
Vol 2015 ◽  
pp. 1-10 ◽  
Author(s):  
Saeed Reza Shahriary ◽  
Mohsen Shahriari ◽  
Rafidah MD Noor
Keyword(s):  
Social Networks ◽  
Community Detection ◽  
Link Prediction ◽  
Large Scale ◽  
Prediction Problem ◽  
Signed Graphs ◽  
Community Based ◽  
Ranking Algorithms ◽  
Detection Approach ◽  
Signed Social Networks

In signed social networks, relationships among nodes are of the types positive (friendship) and negative (hostility). One absorbing issue in signed social networks is predicting sign of edges among people who are members of these networks. Other than edge sign prediction, one can define importance of people or nodes in networks via ranking algorithms. There exist few ranking algorithms for signed graphs; also few studies have shown role of ranking in link prediction problem. Hence, we were motivated to investigate ranking algorithms availed for signed graphs and their effect on sign prediction problem. This paper makes the contribution of using community detection approach for ranking algorithms in signed graphs. Therefore, community detection which is another active area of research in social networks is also investigated in this paper. Community detection algorithms try to find groups of nodes in which they share common properties like similarity. We were able to devise three community-based ranking algorithms which are suitable for signed graphs, and also we evaluated these ranking algorithms via sign prediction problem. These ranking algorithms were tested on three large-scale datasets: Epinions, Slashdot, and Wikipedia. We indicated that, in some cases, these ranking algorithms outperform previous works because their prediction accuracies are better.

A new scalable leader-community detection approach for community detection in social networks

2018 ◽  
Vol 54 ◽  
pp. 41-49 ◽  
Author(s):  
Sara Ahajjam ◽  
Mohamed El Haddad ◽  
Hassan Badir
Keyword(s):  
Social Networks ◽  
Community Detection ◽  
Detection Approach

Community Detection in Large-Scale Social Networks

2018 ◽  
pp. 189-206 ◽  
Author(s):  
S Rao Chintalapudi ◽  
M. H. M. Krishna Prasad
Keyword(s):  
Social Networks ◽  
Community Detection ◽  
Large Scale ◽  
The Other ◽  
Edge Density ◽  
Challenging Problem ◽  
Overlapping Community Detection ◽  
Clustering Problem ◽  
Detection Algorithms ◽  
Overlapping Community

Community Structure is one of the most important properties of social networks. Detecting such structures is a challenging problem in the area of social network analysis. Community is a collection of nodes with dense connections than with the rest of the network. It is similar to clustering problem in which intra cluster edge density is more than the inter cluster edge density. Community detection algorithms are of two categories, one is disjoint community detection, in which a node can be a member of only one community at most, and the other is overlapping community detection, in which a node can be a member of more than one community. This chapter reviews the state-of-the-art disjoint and overlapping community detection algorithms. Also, the measures needed to evaluate a disjoint and overlapping community detection algorithms are discussed in detail.

Genetic algorithm-based community detection in large-scale social networks

2019 ◽  
Vol 32 (13) ◽  
pp. 9649-9665 ◽  
Author(s):  
Ranjan Kumar Behera ◽  
Debadatta Naik ◽  
Santanu Kumar Rath ◽  
Ramesh Dharavath
Keyword(s):  
Genetic Algorithm ◽  
Social Networks ◽  
Community Detection ◽  
Large Scale

Scaling density-based community detection to large-scale social networks via MapReduce framework

2020 ◽  
Vol 38 (2) ◽  
pp. 1663-1674
Author(s):  
Muhammad Abulaish ◽  
Ishfaq Majid Bhat ◽  
Sajid Yousuf Bhat
Keyword(s):  
Social Networks ◽  
Community Detection ◽  
Large Scale ◽  
Mapreduce Framework

scholarly journals Research on Community Detection in Complex Networks Based on Internode Attraction

Entropy ◽  
2020 ◽  
Vol 22 (12) ◽  
pp. 1383
Author(s):  
Jinfang Sheng ◽  
Cheng Liu ◽  
Long Chen ◽  
Bin Wang ◽  
Junkai Zhang
Keyword(s):  
Community Structure ◽  
Complex Networks ◽  
Community Detection ◽  
Large Scale ◽  
Rapid Development ◽  
Structure Detection ◽  
Detection Approach ◽  
Real World Datasets ◽  
New Community ◽  
Large Scale Networks

With the rapid development of computer technology, the research on complex networks has attracted more and more attention. At present, the research directions of cloud computing, big data, internet of vehicles, and distributed systems with very high attention are all based on complex networks. Community structure detection is a very important and meaningful research hotspot in complex networks. It is a difficult task to quickly and accurately divide the community structure and run it on large-scale networks. In this paper, we put forward a new community detection approach based on internode attraction, named IACD. This algorithm starts from the perspective of the important nodes of the complex network and refers to the gravitational relationship between two objects in physics to represent the forces between nodes in the network dataset, and then perform community detection. Through experiments on a large number of real-world datasets and synthetic networks, it is shown that the IACD algorithm can quickly and accurately divide the community structure, and it is superior to some classic algorithms and recently proposed algorithms.

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