Hsun-Ping Hsieh
ABSTRACT
In current social networking service (SNS) such as Facebook, there are diverse kinds of interactions between entity types. One commonly-used activity of SNS users is to track and observe the representative social and temporal behaviors of other individuals. This inspires us to propose a new problem of Temporal Social Behavior Search (TSBS) from social interactions in an information network: given a structural query with associated temporal labels, how to find the subgraph instances satisfying the query structure and temporal requirements? In TSBS, a query can be (a) a topological structure, (b) the partially-assigned individuals on nodes, and/or (c) the temporal sequential labels on edges. The TSBS method consists of two parts: offline mining and online matching. to the former mines the temporal subgraph patterns for retrieving representative structures that match the query. Then based on the given query, we perform the online structural matching on the mined patterns and return the top-k resulting subgraphs. Experiments on academic datasets demonstrate the effectiveness of TSBS.
- A. Inokuchi, T. Washio, and H. Motoda. An Apriori-based algorithm for mining frequent substructures from graph data, In Proc. of the 4th European Conference on Principles of Data Mining and Knowledge Discovery, Lyon, France, 2000, pp. 13--23. Google Scholar
Digital Library
- J. Cheng, J. X. Yu, Bolin. Ding, P. S. Yu, and H. Wang. Fast Graph Pattern Matching. In Proc. of IEEE International Conference on Data Engineering, pp. 913--922, 2008. Google ScholarDigital Library
- H. Tong, B. Gallagher, C. Faloutsos, and T. Eliassi-Rad. Fast Best-Effort Pattern Matching in Large Attributed Graphs. In Proc. of ACM SIGKDD Conference on Knowledge Discovery and Data Mining, pp. 737--746, 2007. Google ScholarDigital Library
- M. Kuramochi, and G. Karypis. Frequent subgraph discovery, In Proc. of IEEE International Conference on Data Mining, California, USA, 2001, pp. 313--320. Google ScholarDigital Library
- R. Agrawal and R. Srikant. Fast algorithms for mining association rules in large databases. In Proc. of ACM VLDB Conference on Very Large data bases, pp. 487--499, 1994. Google ScholarDigital Library
- X. Yan, and J. Han. gSpan: Graph-based substructure pattern mining, In Proc. of IEEE International Conference on Data Mining, pp. 721--724, 2002. Google ScholarDigital Library
Index Terms
- Frequent temporal social behavior search in information networks
Recommendations
Social search behavior in a social Q&A service: goals, strategies, and outcomes
ASIST '15: Proceedings of the 78th ASIS&T Annual Meeting: Information Science with Impact: Research in and for the CommunityRecent advancements in social technologies allow information seekers to reach out to a larger, more distributed group of people online when searching for information. In this study, people's question-asking behavior using a social Q&A service is ...
The value of social search: seeking collective personal experience in social Q&A
ASIST '13: Proceedings of the 76th ASIS&T Annual Meeting: Beyond the Cloud: Rethinking Information BoundariesIn this paper, we examine the value of social question-answering (Q&A) services as a platform for social search. We present a quasi-field study where we instructed 20 study participants to use a social Q&A service, Yahoo! Answers, for a period of one ...
Social Search: Exploring and Searching Social Architectures in Digital Networks
Content authors are increasingly using the Internet to network, providing each other with advice, collaboratively filtering important information, and creating virtual networks of trust. To adequately understand this social cyberspace, we must be able ...
Comments