Query-driven discovery of semantically similar substructures in heterogeneous networks
Proceedings of the 18th ACM SIGKDD international conference on Knowledge discovery and data mining
User guided entity similarity search using meta-path selection in heterogeneous information networks
Proceedings of the 21st ACM international conference on Information and knowledge management
QS-STT: QuadSection clustering and spatial-temporal trajectory model for location prediction
Distributed and Parallel Databases
Familiar strangers detection in online social networks
Proceedings of the 2013 IEEE/ACM International Conference on Advances in Social Networks Analysis and Mining
Journal of Network and Computer Applications
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The popularization of GPS-enabled mobile devices provides social network researchers a taste of cyber-physical social network in advance. Traditional link prediction methods are designed to find friends solely relying on social network information. With location and trajectory data available, we can generate more accurate and geographically related results, and help web-based social service users find more friends in the real world. Aiming to recommend geographically related friends in social network, a three-step statistical recommendation approach is proposed for GPS-enabled cyber-physical social network. By combining GPS information and social network structures, we build a pattern-based heterogeneous information network. Links inside this network reflect both people's geographical information, and their social relationships. Our approach estimates link relevance and finds promising geo-friends by employing a random walk process on the heterogeneous information network. Empirical studies from both synthetic datasets and real-life dataset demonstrate the power of merging GPS data and social graph structure, and suggest our method outperforms other methods for friends recommendation in GPS-based cyber-physical social network.