Multilayer Social Network Overlapping Community Detection Algorithm Based on Trust Relationship

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.

Download Full-text

Overlapping Community Detection Based on Node Importance and Adjacency Information

Security and Communication Networks ◽

10.1155/2021/8690662 ◽

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.

Download Full-text

Overlapping Community Detection Based on Attribute Augmented Graph

Entropy ◽

10.3390/e23060680 ◽

2021 ◽

Vol 23 (6) ◽

pp. 680

Author(s):

Hanyang Lin ◽

Yongzhao Zhan ◽

Zizheng Zhao ◽

Yuzhong Chen ◽

Chen Dong

Keyword(s):

Community Detection ◽

Real World ◽

Detection Algorithm ◽

Overlapping Community Detection ◽

Overlapping Communities ◽

Adjustment Strategy ◽

Topology Information ◽

Overlapping Community ◽

Real World Datasets ◽

Community Detection Algorithm

There is a wealth of information in real-world social networks. In addition to the topology information, the vertices or edges of a social network often have attributes, with many of the overlapping vertices belonging to several communities simultaneously. It is challenging to fully utilize the additional attribute information to detect overlapping communities. In this paper, we first propose an overlapping community detection algorithm based on an augmented attribute graph. An improved weight adjustment strategy for attributes is embedded in the algorithm to help detect overlapping communities more accurately. Second, we enhance the algorithm to automatically determine the number of communities by a node-density-based fuzzy k-medoids process. Extensive experiments on both synthetic and real-world datasets demonstrate that the proposed algorithms can effectively detect overlapping communities with fewer parameters compared to the baseline methods.

Download Full-text

A Semantic Community Detection Algorithm Based on Quantizing Progress

Complexity ◽

10.1155/2019/3475458 ◽

2019 ◽

Vol 2019 ◽

pp. 1-13 ◽

Cited By ~ 1

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.

Download Full-text

An overlapping community detection algorithm based on node distance of line graph

Modern Physics Letters B ◽

10.1142/s0217984919503226 ◽

2019 ◽

Vol 33 (26) ◽

pp. 1950322 ◽

Cited By ~ 1

Author(s):

Guishen Wang ◽

Yuanwei Wang ◽

Kaitai Wang ◽

Zhihua Liu ◽

Lijuan Zhang ◽

...

Keyword(s):

Community Detection ◽

Clustering Algorithm ◽

Line Graph ◽

Detection Algorithm ◽

Density Peak ◽

Overlapping Community Detection ◽

Overlapping Communities ◽

Normalized Mutual Information ◽

Overlapping Community ◽

Community Detection Algorithm

Overlapping community detection is a hot topic in research of complex networks. Link community detection is a popular approach to discover overlapping communities. Line graph is a widely used model in link community detection. In this paper, we propose an overlapping community detection algorithm based on node distance of line graph. Considering topological structure of links in graphs, we use line graph to transform links of graph into nodes of line graph. Then, we calculate node distance of line graph according to their dissimilarity. After getting distance matrix, we proposed a new [Formula: see text] measure based on nodes of line graph and combine it with clustering algorithm by fast search and density peak to identify node communities of line graph. Finally, we acquire overlapping node communities after transforming node communities of line graph back to graph. The experimental results show that our algorithm achieves a higher performance on normalized mutual information metric.

Download Full-text