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《山东大学学报(理学版)》 ›› 2021, Vol. 56 ›› Issue (9): 1-12,20.doi: 10.6040/j.issn.1671-9352.4.2021.174

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基于注意力网络特征的社区发现算法

王静红1,2(),梁丽娜1,李昊康1,周易3,*()   

  1. 1. 河北师范大学计算机与网络空间安全学院, 河北 石家庄 050024
    2. 河北省供应链大数据分析与数据安全工程研究中心, 河北 石家庄 050024
    3. 上海海关科技处, 上海 200002
  • 收稿日期:2021-06-02 出版日期:2021-09-20 发布日期:2021-09-13
  • 通讯作者: 周易 E-mail:wangjinghong@126.com;zhouwangyilang@126.com
  • 作者简介:王静红(1967—), 女, 博士, 教授, 研究方向为人工智能、大数据与数据挖掘、模式识别等. E-mail: wangjinghong@126.com
  • 基金资助:
    河北省自然科学基金资助项目(F2019205303);河北省引进留学人员资助项目(C20200340)

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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摘要：

现实世界中许多网络都是根据社区结构紧密组织起来的, 发现社区对于了解复杂网络的结构及其关系有很大的帮助, 文中提出了一种基于注意力网络特征的社区发现(community discovery algorithm based on attention network features, CANF)算法, 利用标记节点频率和反示例节点频率度量初始网络标记特征, 并且引入注意力机制, 对示例节点的每个邻居节点更好地分配权重, 将初始权重与分配权重相结合, 使初始度量的网络特征获取更多与目标有关的细节信息。文中通过分配的注意力网络特征进行复杂网络预处理以及社区博弈归并, 于真实网络中进行验证, 实验结果表明, CANF算法在准确度、模块度以及运行时间方面优于其他社区发现算法。

关键词: 复杂网络, 注意力网络特征, 社区发现, 网络预处理, 社区博弈归并

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

中图分类号: 

  • TP391

图1

注意力网络特征"

图2

CANF算法模型框架"

表1

实验数据集信息"

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

图3

CANF算法在空手道俱乐部网络上划分的社区图"

图4

空手道俱乐部网络的自然划分社区图"

表2

CANF算法与其他算法在真实网络上的NMI"

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

表3

LPA算法在真实网络上的NMI"

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

图5

CANF算法与其他算法在真实网络上的NMI对比"

图6

CANF算法在海豚网络上划分的社区图"

表4

CANF算法与其他算法在各真实网络数据集中的模块度Q"

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

图7

CANF算法与其他算法在各数据集上的模块度Q对比图"

图8

CANF算法在形容词和名词邻接网络上划分的社区图"

表5

CANF算法与其他算法在各数据集上的社区数量"

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

表6

N次迭代后LPA在各数据集上的社区数量"

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

表7

CANF算法与其他算法在各真实网络上的运行时间"

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

图9

CANF算法与其他算法在各真实网络上的运行时间对比图"

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