JOURNAL OF SHANDONG UNIVERSITY(NATURAL SCIENCE) ›› 2021, Vol. 56 ›› Issue (9): 1-12,20.doi: 10.6040/j.issn.1671-9352.4.2021.174

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Community discovery algorithm based on attention network feature

Jing-hong WANG1,2(),Li-na LIANG1,Hao-kang LI1,Yi ZHOU3,*()   

  1. 1. School of Computer and Cyber Security, Hebei Normal University, Shijiazhuang 050024, Hebei, China
    2. Hebei Provincial Engineering Research Center for Supply Chain Big Data Analytics & Data Security, Shijiazhuang 050024, Hebei, China
    3. Science & Technology Division, Shanghai Customs District P R China, Shanghai 200002, China
  • Received:2021-06-02 Online:2021-09-20 Published:2021-09-13
  • Contact: Yi ZHOU E-mail:wangjinghong@126.com;zhouwangyilang@126.com

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Abstract:

In the real world, many networks are closely organized according to the community structure. It has been found that the community is of great help in understanding the structure and relationship of complex networks. This paper proposes a community discovery algorithm based on attention network features (CANF algorithm). It uses the label node frequency and the inverse example node frequency to measure the initial network label characteristics, and introduces the attention mechanism, and updates each neighbor node of the example node. Combined the initial weight with the assigned weight, the network features of the initial measure can obtain more detailed information related to the target. In this paper, complex network preprocessing and community game merge are carried out through distributed attention network features. The experimental results show that the accuracy, modularity and running time of the community discovery algorithm based on attention network features are better than other community discovery algorithms.

Key words: complex network, attention network feature, community discovery, network preprocessing, community game merge

CLC Number: 

  • TP391

Fig.1

Attentional network feature"

Fig.2

CANF algorithm model framework"

Table 1

Experimental dataset information"

数据集信息真实网络数据集
karate dolphin lesmis football polbooks adjnoun netscience
节点数量 34 62 77 115 105 112 1589
边数量 78 159 508 613 441 425 2742

Fig.3

CANF algorithm divides community map in karate club network"

Fig.4

Natural division of karate club network community map[30]"

Table 2

NMI of CANF algorithm and other algorithms on real networks"

数据集 CANF算法 GN算法 Fast-Newman算法 SLPA
karate 0.838 0.652 0.621 0.414
dolphin 0.814 0.554 0.814 0.560

Table 3

NMI of LPA algorithm on real network"

数据集迭代次数
1 2 3
karate 0.700 1 6.4229E-16
dolphin 0.513 0.649 1

Fig.5

NMI comparison graph of CANF algorithm and other algorithms on real networks"

Fig.6

CANF algorithm divides community map in dolphin network"

Table 4

Modularity Q of CANF algorithm and other algorithms on each real network"

数据集 CANF算法 GN算法 LPA Fast-Newman算法 SLPA
karate 0.360 0.263 0.372 0.253 0.068
dolphin 0.380 0.484 0.379 0.450 0.230
lesmis 0.398 0.448 0.317 0.394 0.154
polbooks 0.187 0.312 0.444 0.172 0.264
adjnoun 0.208 0.136 4.046e-17 0.243 0.102

Fig.7

Comparison chart of modularity Q between CANF algorithm and other algorithms on real networks"

Fig.8

CANF algorithm divides community map in polbook network"

Table 5

Number of communities for CANF algorithm and other algorithms on each real network"

算法 karate dolphin lesmis football polbooks adjnoun netscience
CANF算法 2 2 3 7 3 4 50
GN算法 2 2 4 12 3 9 406
Fast-Newman算法 12 1 4 5 79 9 200
SLPA 3 7 5 9 8 4 349

Table 6

Number of communities after LPA algorithm is executed N times on real networks"

迭代次数 karate dolphin lesmis football polbooks adjnoun
1 2 2 3 12 4 1
2 3 3 4 10 1 1
3 4 4 3 9 3 1
4 1 5 4 11 2 1
5 — — 2 7 — 1
6 — — 3 8 — 1

Table 7

Running times of the CANF algorithm and other algorithms on each real network  s"

karate dolphin lesmis football polbooks adjnoun netscience
CANF算法 0.124 0.306 0.309 0.876 0.741 0.487 173.282
GN算法 6.374 45.981 126.757 888.326 513.892 668.046 3.024E+05
LPA 0.066 0.083 0.154 0.190 0.103 0.121 50865.300
Fast-Newman算法 0.389 1.594 9.805 9.312 7.114 8.386 9335.170
SLPA 0.860 1.585 7.718 5.956 3.583 3.649 23.364

Fig.9

Running time comparison figure for CANF algorithm and other algorithms on real networks"

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