基于时间加权的重叠社区检测算法研究

李慧; 马小平; 张舒; 施珺; 李存华; 仲兆满

doi:10.16383/j.aas.c180559

基于时间加权的重叠社区检测算法研究

doi: 10.16383/j.aas.c180559

李慧^{1, 4,},
马小平^2, ,,
张舒^3,,
施珺^1,,
李存华^1,,
仲兆满^{1, 4,}

1.
江苏海洋大学计算机工程学院连云港 222005
2.
中国矿业大学信息与控制工程学院徐州 221008
3.
江苏海洋大学商学院连云港 222005
4.
江苏省海洋资源开发研究院连云港 222005

基金项目:

国家自然科学基金 61873105

江苏省"333工程" 培养对象, 连云港市第六期"521工程" 培养对象, 江苏省"333工程" 项目 BRA2020261

教育部协同育人项目 201902159041

江苏省教改项目 JGX2019011ZZ

江苏省高等学校自然科学研究项目 19KJB520004

连云港高新区科技计划项目 ZD201912

详细信息

作者简介:
李慧  博士, 江苏海洋大学计算机工程学院副教授. 主要研究方向为个性化推荐, 社会网络分析. E-mail: shufanzs@126.com

张舒  江苏海洋大学商学院讲师. 主要研究方向为智能信息处理. E-mail: lih@cumt.edu.cn

施珺  江苏海洋大学计算机工程学院教授. 主要研究方向为智能信息处理. E-mail: sj_lfg@hotmail.com

李存华  博士, 江苏海洋大学计算机工程学院教授. 主要研究方向为数据挖掘. E-mail: cli2000@126.com

仲兆满  博士, 江苏海洋大学计算机工程学院副教授. 主要研究方向为中文信息处理. E-mail: zhongzhaoman@163.com

通讯作者:
马小平博士, 中国矿业大学信电学院教授. 主要研究方向为智能计算. 本文通信作者. E-mail: xpma@cumt.edu.cn

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出版历程
- 收稿日期: 2018-10-30
- 录用日期: 2019-05-19
- 刊出日期: 2021-04-01

Research of Overlap Community Detection Algorithm Based on Time-Weighted

LI Hui^{1, 4
,},
MA Xiao-Ping^{2
, ,},
ZHANG Shu^3
,,
SHI Jun^1
,,
LI Cun-Hua^1
,,
ZHONG Zhao-Man^{1, 4
,}

1.
School of Computer Engineering, Jiangsu Ocean University, Lianyungang 222005
2.
School of Information and Control Engineering, China University of Mining and Technology, Xuzhou 221008
3.
School of Business, Jiangsu Ocean University, Lianyungang 222005
4.
Jiangsu Institute of Marine Resources Development, Lianyungang 222005

Funds:

National Natural Science Foundation of China 61873105

"333 Project" cultivation object of Jiangsu province, "521 Project" Cultivation Object of Lianyungang, "333 Project" of Jiangsu Province BRA2020261

Ministry of Education Collaborative Education Project 201902159041

Education Reform Project of Jiangsu Province JGX2019011ZZ

Natural Science Research Project of Higher Education of Jiangsu Province 19KJB520004

Lianyungang High-tech Zone Science and Technology Project ZD201912

More Information

Author Bio:
LI Hui  Ph. D., associate professor at the School of Computer Engineering, Jiangsu Ocean University. Her research interest covers personality recommendation and social network analysis

ZHANG Shu  Lecturer at the School of Business, Jiangsu Ocean University. Her main research interest is information processing

SHI Jun  Professor in the Department of Computer Science, Jiangsu Ocean University. Her main research interest is information processing

LI Cun-Hua  Ph. D., professor in the Department of Computer Science, Jiangsu Ocean University. His main research interest is data mining

ZHONG Zhao-Man  Ph. D., associate professor in the Department of Computer Science, Jiangsu Ocean University. His main research interest is Chinese information processing

Corresponding author: MA Xiao-Ping Ph. D., professor at the School of Information and Electrical Engineering, China University of Mining & Technology. His main research interest is intelligent computing. Corresponding author of this paper

摘要

摘要: 随着网络结构的不断扩大和日益复杂, 重叠社区发现技术对挖掘复杂网络深层潜在结构具有重要意义. 本文提出一种基于时间加权的重叠社区检测算法. 该方法考虑了用户兴趣的时间因素, 构建带有时间加权链接的用户-用户图. 接着, 基于网络节点的影响力计算用户全局相似度, 在此基础上通过计算节点的中心度作为度量节点对社区结构影响力的重要性指标, 从而提出一种社区中心点的选取方法. 最后, 通过效用函数的迭代计算实现重叠社区检测. 利用人工网络和真实网络对提出的算法进行验证, 实验结果表明: 相对于传统的社区发现方法, 该算法在社区发现质量和计算效率方面都优于许多已有重叠社区发现算法.
- 社会网络 /
- 加权 /
- 重叠 /
- 社区 /
- 检测
Abstract: With the continuous expansion and complexity of network structure, overlapping community discovery technology is of great signiflcance to excavate the deep potential structure of complex network. This article presents an overlapping community detection algorithm based on time time-weighted. Considering the time factor of user interest, this method constructs a user-user graph with time-weighted links. Then, the global similarity of users is calculated based on the influence of network nodes. On this basis, the centrality of nodes is calculated as an important index to measure the impact of nodes on community structure, and a method to select community centers is proposed. Finally, overlapping community detection is realized by iteration of utility function. The proposed algorithm is validated by artiflcial network and real network. The experimental results show that compared with traditional community discovery methods, the proposed algorithm outperforms many existing overlapping community discovery algorithms in terms of community discovery quality and computational efficiency.
- Social network /
- time-weighted /
- overlap /
- community /
- detection
Recommended by Associate Editor DUAN Shu-Kai
注释:

1) 本文责任编委段书凯

HTML全文

图 1 Polblogs数据集中参数α对Q值的影响结果

Fig. 1 The influence of different α on the Q in Polblogs data set

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图 2 DBLP数据集中参数α对Q值的影响结果

Fig. 2 The influence of different α on the Q in DBLP data set

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图 3 重叠社区的模块度随社区数量的变化情况

Fig. 3 The influence of different community number on the K

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图 4 社区数量随阈值β的变化情况

Fig. 4 The influence of different β on different community number K

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图 5 真实数据集上各算法性能对比实验

Fig. 5 Comparison results of different algorithms on real networks

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图 6 不同算法运行时间比较

Fig. 6 Execution time comparison of different algorithms

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表 1 LFR基准网络生成参数说明

Table 1 Parameter setting of LFR benchmark network generation

参数	说明
N	网络的节点数目
k	网络中节点的平均度数
C_min	最小社区的节点数目
C_max	最大社区的节点数目
on	重叠节点的个数
om	重叠节点所从属的社区个数
mu	社区混合参数

下载: 导出CSV

表 2 人工网络数据集

Table 2 Artificial network datasets

编号	N	k	C_min	C_max	mu	on	om
S₁	10 000	20	50	100	0.1~0.7	1 000	3
S₂	100 000	20	100	200	0.1~0.7	5 000	2
S₃	10 000	20	100	200	0.1	1 000	2~7

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表 3 真实数据集

Table 3 Real datasets

编号	名称	节点数	边数	平均度
R1	Karate	34	78	4.75
R2	Doplphins	62	159	5.13
R3	Polbooks	105	441	8.40
R4	Football	115	613	10.66
R5	Folbogs	1 490	16 715	22.44
R6	DBLP	4 000	8 301	2.52

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表 4 mu在不同取值时各算法在人工网络S1上的NMI实验结果

Table 4 NMI experimental results of different algorithms on S1 under different mu value

mu	0.1	0.2	0.3	0.4	0.5	0.6	0.7
TWOCD	0.53	0.45	0.34	0.24	0.23	0.22	0.22
COPRA	0.85	0.79	0.71	0.62	0.42	0.22	0.22
CPM	0.82	0.8	0.62	0.48	0.21	0.22	0.22
LFM	0.52	0.42	0.35	0.22	0.22	0.22	0.22

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表 5 om在不同取值时各算法在人工网络S2上的NMI实验结果

Table 5 NMI experimental results of different algorithms on S2 under different om value

om	2	3	4	5	6	7	8
TWOCD	0.92	0.95	0.88	0.84	0.78	0.72	0.75
COPRA	0.93	0.9	0.83	0.78	0.71	0.65	0.6
CPM	0.92	0.87	0.81	0.78	0.69	0.62	0.62
LFM	0.76	0.68	0.66	0.67	0.65	0.66	0.5

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表 6 on在不同取值时各算法在人工网络S3上的NMI实验结果

Table 6 NMI experimental results of different algorithms on S3 under different on value

on	1 000	2 000	3 000	4 000	5 000	6 000	7 000
TWOCD	0.95	0.95	0.89	0.76	0.67	0.56	0.38
COPRA	0.89	0.83	0.78	0.58	0.32	0.21	0.21
CPM	0.82	0.84	0.79	0.7	0.6	0.47	0.28
LFM	0.43	0.33	0.23	0.23	0.24	0.24	0.24

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表 7 真实数据集上各算法在不同参数取值下性能对比结果

Table 7 Comparison results of different algorithms on different parameter in real networks

Data set	LFM		COPRA		CPM		TWOCD
Data set	α	Q	v	Q	k	Q	α, β	Q
Karate	0.8	0.813	2	0.825	2	0.823	0.4, 0.8	0.8463
Football	1.1	0.645	2	0.656	4	0.707	0.4, 0.8	0.7246
Doplphins	0.8	0.812	6	0.821	5	0.924	0.3, 0.7	0.9005
Polbooks	0.9	0.634	8	0.717	8	0.795	0.4, 0.9	0.7342
Folbogs	1.4	0.122	2	0.466	6	0.625	0.4, 0.8	0.6257
DBLP	0.8	0.787	9	0.745	3	0.797	0.4, 0.8	0.8143

下载: 导出CSV

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