Community search method based on motif connectivity

doi:10.11772/j.issn.1001-9081.2022060941

Journal of Computer Applications ›› 2023, Vol. 43 ›› Issue (7): 2190-2199.DOI: 10.11772/j.issn.1001-9081.2022060941

• Data science and technology • Previous Articles

Community search method based on motif connectivity

Ming DU, Wanli GU, Junfeng ZHOU(), Zhijun WANG

College of Computer Science and Technology，Donghua University，Shanghai 201620，China

Received:2022-06-28 Revised:2022-09-14 Accepted:2022-09-22 Online:2022-10-11 Published:2023-07-10
Contact: Junfeng ZHOU
About author:DU Ming， born in 1975， Ph. D.， associate professor. His research interests include natural language processing， information query， data analysis.
GU Wanli， born in 1998， M. S. His research interests include community query processing.
ZHOU Junfeng， born in 1977， Ph. D.， professor. His research interests include information retrieval， query processing， optimization of graphical data.
WANG Zhijun， born in 1974， Ph. D.， associate professor. His research interests include internet of things information service， information retrieval.
Supported by:
Natural Science Foundation of Shanghai(20ZR1402700)

基于motif连通性的社区搜索方法

杜明, 顾万里, 周军锋(), 王志军

东华大学计算机科学与技术学院，上海 201620

通讯作者: 周军锋
作者简介:杜明（1975—），男，黑龙江鸡西人，副教授，博士，主要研究方向：自然语言处理、信息查询、数据分析；
顾万里（1998—），男，上海人，硕士，主要研究方向：社区查询处理；
周军锋（1977—），男，陕西西安人，教授，博士，主要研究方向：信息检索、图形数据的查询处理优化；
王志军（1974—），男，黑龙江五常人，副教授，博士，主要研究方向：物联网信息服务、信息检索。
基金资助:
上海市自然科学基金资助项目(20ZR1402700)

Abstract

Abstract:

The goal of community search is to obtain compact subgraphs containing query vertices from data graphs， and community search is widely used in sociology， biology， and other fields. In view of the fact that the basic connectivity structures of the existing community models based on subgraph connectivity are all completely connected graphs， which cannot meet the needs of users for the diversity of community structures in practical applications， a community search method based on motif connectivity was proposed， including Motif-Connective Community （MCC） model based on motif connectivity and two corresponding community search algorithms — MPCS （Motif-Processed Community Search） algorithm and MP-index based community search algorithm. MCC model was able to help users freely specify the basic connectivity structure of community， and MPCS algorithm was able to be used to solve the search problem of MCC. Furthermore， two pruning optimization techniques were proposed for the motif instance search process and the belonged community judgment process. Finally， the MP-index forest was designed to avoid redundant traversal operations in the process of community search. Experimental results on multiple real datasets show that the pruning optimization can reduce the running time of MPCS algorithm by 60% to 85%， and the efficiency of the community search algorithm based on MP-index forest is improved by two to three orders of magnitude compared with the efficiency of MPCS algorithm added with pruning optimization. It can be seen that the proposed method has practical application values in commodity recommendation， social network and other issues.

Key words: community search, motif connectivity, subgraph connectivity community, pruning optimization, diversity of community structure

摘要：

社区搜索的目标是从数据图中得到包含查询顶点的紧密子图，在社会学、生物学等领域有着广泛应用。针对现有基于子图连通性的社区模型的基础连通结构都是完全连通图，无法满足实际应用中用户对社区结构多样性的需求的问题，提出一种基于motif连通性的社区搜索方法，其中包括基于motif连通性的社区（MCC）模型以及两个相应的社区搜索算法——MPCS （Motif-Processed Community Search）算法和基于MP-index的社区搜索算法。MCC模型可以协助用户自由指定社区的基础连通结构，MPCS算法可以用来解决MCC的搜索问题。此外，提出两个分别针对motif实例搜索过程及所属社区判断过程的剪枝优化技术。最后，设计了MP-index以避免社区搜索过程中的冗余遍历操作。在多个真实数据集上进行实验的结果表明：剪枝优化可以使MPCS算法的耗时减少60%~85%，而基于MP-index的社区搜索算法相较于加入剪枝优化的MPCS算法，效率提升普遍达到了2~3个数量级。可见，所提方法在商品推荐和社交网络等问题上有着实际应用价值。

关键词: 社区搜索, motif连通性, 子图连通性社区, 剪枝优化, 社区结构多样性

CLC Number:

TP301.6

Ming DU, Wanli GU, Junfeng ZHOU, Zhijun WANG. Community search method based on motif connectivity[J]. Journal of Computer Applications, 2023, 43(7): 2190-2199.

杜明, 顾万里, 周军锋, 王志军. 基于motif连通性的社区搜索方法[J]. 《计算机应用》唯一官方网站, 2023, 43(7): 2190-2199.

Figures/Tables 13

Fig. 1 Undirected graph G

Fig. 2 Examples of motif

Fig. 3 Example of MOD-index

Fig. 4 Example of hitching situation

Fig. 5 Example of MP-index for undirected graph Gin figure 1 with motif of figure 2（d） and maximum threshold of 4

Fig. 6 Intermediate processes of index construction

Tab. 1 Statistics for datasets

数据集	$V$	$E$
Ecoo	12 620	13 350
Fb-sports	13 617	86 890
Yago	6 642	42 392
10go-uniprot	469 526	3 476 397
Cit-Patents	3 774 768	16 518 947
Arxiv	6 000	133 414
Soc-academia	201 211	2 491 576
Citeseer	10 720	44 258
Soc-flixster	3 365 623	7 989 191

Tab. 1 Statistics for datasets

数据集	$V$	$E$
Ecoo	12 620	13 350
Fb-sports	13 617	86 890
Yago	6 642	42 392
10go-uniprot	469 526	3 476 397
Cit-Patents	3 774 768	16 518 947
Arxiv	6 000	133 414
Soc-academia	201 211	2 491 576
Citeseer	10 720	44 258
Soc-flixster	3 365 623	7 989 191

Fig. 7 Running time comparison of MPCS， two optimization algorithms and MPCS* algorithm

Tab. 2 Running time comparison of MPCS* algorithm and MP-index based community search algorithm

数据集	MPCS^*	MP-index
Ecoo	3.58	0.003 8
Yago	2.03	0.002 3
10go-uniprot	100.25	0.021 0
Cit-Patents	152.67	0.039 0
Arxiv	61.80	0.033 0
Soc-academia	181.59	0.071 0
Citeseer	31.05	0.006 7
Soc-flixster	13.04	0.001 2

Fig. 8 Running time comparison of MPCS* algorithm and MP-index based community search algorithm under different thresholds

Fig. 9 Community structure based on k-clique

Fig. 10 Community structure based on k-truss

Fig. 11 Community structure based on MCC

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Community search method based on motif connectivity

基于motif连通性的社区搜索方法

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