ICDM '01 Proceedings of the 2001 IEEE International Conference on Data Mining
An Apriori-Based Algorithm for Mining Frequent Substructures from Graph Data
PKDD '00 Proceedings of the 4th European Conference on Principles of Data Mining and Knowledge Discovery
Fast Algorithms for Mining Association Rules in Large Databases
VLDB '94 Proceedings of the 20th International Conference on Very Large Data Bases
gSpan: Graph-Based Substructure Pattern Mining
ICDM '02 Proceedings of the 2002 IEEE International Conference on Data Mining
Fast best-effort pattern matching in large attributed graphs
Proceedings of the 13th ACM SIGKDD international conference on Knowledge discovery and data mining
ICDE '08 Proceedings of the 2008 IEEE 24th International Conference on Data Engineering
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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.