Journal of Computer Science and Technology ›› 2019, Vol. 34 ›› Issue (1): 170-184.doi: 10.1007/s11390-019-1905-0

Special Issue: Artificial Intelligence and Pattern Recognition; Data Management and Data Mining

• Data Management and Data Mining • Previous Articles     Next Articles

Who Should Be Invited to My Party: A Size-Constrained k-Core Problem in Social Networks

Yu-Liang Ma1, Member, CCF, Ye Yuan1, Member, CCF, ACM, IEEE, Fei-Da Zhu2, Member, ACM, IEEE Guo-Ren Wang3, Member, CCF, ACM, IEEE, Jing Xiao4, and Jian-Zong Wang4, Member, CCF   

  1. 1 School of Computer Science and Engineering, Northeastern University, Shenyang 110169, China;
    2 School of Information Systems, Singapore Management University, Singapore 188065, Singapore;
    3 School of Computer Science and Technology, Beijing Institute of Technology, Beijing 100081, China;
    4 Ping An Technology (Shenzhen) Co., Ltd, Shenzhen 518048, China
  • Received:2017-11-28 Revised:2018-09-20 Online:2019-01-05 Published:2019-01-12
  • About author:Yu-Liang Ma received his B.S. degree in computer science from the College of Computer Science and Engineering, Northeastern University, Shenyang, in 2013. Currently, he is a Ph.D. candidate of Northeastern University, Shenyang. His main research interests include graph databases, location-based social networks, and social network analysis.
  • Supported by:
    This research is partially supported by the National Research Foundation, Prime Ministers Office, Singapore, under its International Research Centres in Singapore Funding Initiative and Pinnacle Lab for Analytics at Singapore Management University, the National Natural Science Foundation of China under Grant Nos. 61572119, 61622202, 61732003, 61729201, 61702086, and U1401256, and the Fundamental Research Funds for the Central Universities of China under Grant No. N150402005.

In this paper, we investigate the problem of a size-constrained k-core group query (SCCGQ) in social networks, taking both user closeness and network topology into consideration. More specifically, SCCGQ intends to find a group of h users that has the highest social closeness while being a k-core. SCCGQ can be widely applied to event planning, task assignment, social analysis, and many other fields. In contrast to existing work on the k-core detection problem, which aims to find a k-core in a social network, SCCGQ not only focuses on k-core detection but also takes size constraints into consideration. Although the conventional k-core detection problem can be solved in linear time, SCCGQ has a higher complexity. To solve the problem of SCCGQ, we propose a Blast Scatter (BS) algorithm, which appoints the query node as the center to begin outward expansions via breadth search. In each outward expansion, BS finds a new center through a greedy strategy and then selects multiple neighbors of the center. To speed up the BS algorithm, we propose an advanced search algorithm, called Bounded Extension (BE). Specifically, BE combines an effective social distance pruning strategy and a tight upper bound of social closeness to prune the search space considerably. In addition, we propose an offline social-aware index to accelerate the query processing. Finally, our experimental results demonstrate the efficiency and effectiveness of our proposed algorithms on large real-world social networks.

Key words: group query; k-core; social analysis; social network;

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