Community detection with attributed random walk via seed replacement

2022 ◽  
Vol 16 (5) ◽  
Author(s):  
Yang Chang ◽  
Huifang Ma ◽  
Liang Chang ◽  
Zhixin Li
Keyword(s):  
Random Walk ◽  
Community Detection

An algorithm Walktrap-SPM for detecting overlapping community structure

2017 ◽  
Vol 31 (15) ◽  
pp. 1750121 ◽  
Author(s):  
Fang Hu ◽  
Youze Zhu ◽  
Yuan Shi ◽  
Jianchao Cai ◽  
Luogeng Chen ◽  
...  
Keyword(s):  
Community Structure ◽  
Random Walk ◽  
Community Detection ◽  
Real World ◽  
Overlapping Community Detection ◽  
Overlapping Communities ◽  
Running Time ◽  
Overlapping Community ◽  
Negative Links ◽  
Novel Algorithm

In this paper, based on Walktrap algorithm with the idea of random walk, and by selecting the neighbor communities, introducing improved signed probabilistic mixture (SPM) model and considering the edges within the community as positive links and the edges between the communities as negative links, a novel algorithm Walktrap-SPM for detecting overlapping community is proposed. This algorithm not only can identify the overlapping communities, but also can greatly increase the objectivity and accuracy of the results. In order to verify the accuracy, the performance of this algorithm is tested on several representative real-world networks and a set of computer-generated networks based on LFR benchmark. The experimental results indicate that this algorithm can identify the communities accurately, and it is more suitable for overlapping community detection. Compared with Walktrap, SPM and LMF algorithms, the presented algorithm can acquire higher values of modularity and NMI. Moreover, this new algorithm has faster running time than SPM and LMF algorithms.

Memory-based random walk for multi-query local community detection

2019 ◽  
Vol 62 (5) ◽  
pp. 2067-2101
Author(s):  
Yuchen Bian ◽  
Dongsheng Luo ◽  
Yaowei Yan ◽  
Wei Cheng ◽  
Wei Wang ◽  
...  
Keyword(s):  
Random Walk ◽  
Community Detection ◽  
Local Community ◽  
Local Community Detection

scholarly journals Random-Walk Graph Embeddings and the Influence of Edge Weighting Strategies in Community Detection Tasks

2021 ◽  
Author(s):  
Andreas Kosmatopoulos ◽  
Kostas Loumponias ◽  
Despoina Chatzakou ◽  
Theodora Tsikrika ◽  
Stefanos Vrochidis ◽  
...  
Keyword(s):  
Random Walk ◽  
Community Detection ◽  
Graph Embeddings ◽  
Edge Weighting

Community Detection Using Restrained Random-walk Similarity

2019 ◽  
pp. 1-1 ◽  
Author(s):  
Makoto Okuda ◽  
Shinichi Satoh ◽  
Yoichi Sato ◽  
Yutaka Kidawara
Keyword(s):  
Random Walk ◽  
Community Detection

An improved label propagation algorithm based on node importance and random walk for community detection

2017 ◽  
Vol 31 (14) ◽  
pp. 1750162 ◽  
Author(s):  
Tianren Ma ◽  
Zhengyou Xia
Keyword(s):  
Random Walk ◽  
Community Detection ◽  
High Efficiency ◽  
Linear Time ◽  
Structural Characteristics ◽  
Rapid Development ◽  
Time Algorithm ◽  
Label Propagation ◽  
Node Importance ◽  
Propagation Algorithm

Currently, with the rapid development of information technology, the electronic media for social communication is becoming more and more popular. Discovery of communities is a very effective way to understand the properties of complex networks. However, traditional community detection algorithms consider the structural characteristics of a social organization only, with more information about nodes and edges wasted. In the meanwhile, these algorithms do not consider each node on its merits.Label propagation algorithm (LPA) is a near linear time algorithm which aims to find the community in the network. It attracts many scholars owing to its high efficiency. In recent years, there are more improved algorithms that were put forward based on LPA. In this paper, an improved LPA based on random walk and node importance (NILPA) is proposed. Firstly, a list of node importance is obtained through calculation. The nodes in the network are sorted in descending order of importance. On the basis of random walk, a matrix is constructed to measure the similarity of nodes and it avoids the random choice in the LPA. Secondly, a new metric IAS (importance and similarity) is calculated by node importance and similarity matrix, which we can use to avoid the random selection in the original LPA and improve the algorithm stability.Finally, a test in real-world and synthetic networks is given. The result shows that this algorithm has better performance than existing methods in finding community structure.

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