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Data Analysis and Knowledge Discovery  2021, Vol. 5 Issue (5): 41-50    DOI: 10.11925/infotech.2096-3467.2020.1208
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Fuzzy Overlapping Community Detection Algorithm Based on Node Vector Representation
Chen Wenjie(),Wen Yi,Yang Ning
Chengdu Library and Information Center, Chinese Academy of Sciences, Chengdu 610041, China
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[Objective] This paper proposed a fuzzy community partition algorithm based on node vector representation,aiming to solve the problems of poor efficiency and accuracy of existing fuzzy overlapping community partition algorithms. [Methods] Firstly, the random walk strategy guided by node importance is used to generate the walk sequence, and then the skip-gram model is used to train the node vector. Then, the Gaussian mixture model is introduced into the community partition to realize the multi peak node data fitting. Finally, the optimal number of communities is obtained by maximizing the modularity. [Results] Compared with the classical community detection method, the EQ values of the algorithm on the real network jazz and artificial network N1 (mu = 0.5) are increased by 7.0% and 9.7% respectively, which can more accurately detect the community structure in the network. [Limitations] In the vector representation learning, only the topological structure information of complex network is considered, while the node attribute information and edge label information are ignored. [Conclusions] The fuzzy overlapping community detection algorithm based on node vector representation can effectively complete the community division task of complex network.

Key wordsComplex Network      Community Structure      Representation Learning      Random Walk     
Received: 03 December 2020      Published: 27 May 2021
ZTFLH:  TP393  
Fund:The work is supported by the 13th Five-year Informatization Plan of Chinese Academy of Sciences(XXH13506)
Corresponding Authors: Chen Wenjie     E-mail:

Cite this article:

Chen Wenjie,Wen Yi,Yang Ning. Fuzzy Overlapping Community Detection Algorithm Based on Node Vector Representation. Data Analysis and Knowledge Discovery, 2021, 5(5): 41-50.

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Flow Chart of FONV Algorithm
Skip-gram Model(c=2)
网络名 节点数 边数
Football 115 613
Jazz 198 5 483
Netscience 1 461 2 742
Real Network
参数 N1 N2
节点数目 500 5 000
节点平均度 30 200
节点最大度 60 500
社区最小节点数目 50 300
社区最大节点数目 100 1 000
混合参数mu 0~1 0~1
Artificial Network
Visualization of Node Vector
算法 Football Jazz Netscience N1 N2
NiWalk2Vec 1.71s 3.25s 34.14s 9.31s 5.96min
NMF 0.56s 1.01s 101.35s 2.76s 16.51min
Spectral Mapping 0.06s 0.09s 86.92s 0.58s 32.82min
Comparison of Average Running Time
算法 Football Jazz Netscience
GCE 59.5% 28.2% 54.7%
COPRA 60.3% 41.4% 74.9%
BMLPA 60.1% 20.6% 71.4%
CPM 35.7% 9.4% 75.4%
G-FCM 60.4% 44.3% 79.2%
Comparison of EQ Value
EQ and NMI on Artificial Network
算法 EQ NMI 运行时间
NiWalk2Vec 35.1% 100% 9.31s
DeepWalk 33.4% 98.2% 7.46s
Comparison Between NiWalk2Vec and DeepWalk
向量维度 EQ NMI
5 28.9% 77.1%
10 32.8% 96.5%
20 33.0% 99.3%
50 35.1% 100%
100 30.7% 94.7%
EQ and NMI Vary with Vector Dimensions
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