Discovering shared interests using graph analysis
Communications of the ACM - Special issue on internetworking
Mining the network value of customers
Proceedings of the seventh ACM SIGKDD international conference on Knowledge discovery and data mining
Social net: using patterns of physical proximity over time to infer shared interests
CHI '02 Extended Abstracts on Human Factors in Computing Systems
Mining knowledge-sharing sites for viral marketing
Proceedings of the eighth ACM SIGKDD international conference on Knowledge discovery and data mining
AAMAS '03 Proceedings of the second international joint conference on Autonomous agents and multiagent systems
Sensing and Modeling Human Networks using the Sociometer
ISWC '03 Proceedings of the 7th IEEE International Symposium on Wearable Computers
Unsupervised Link Discovery in Multi-relational Data via Rarity Analysis
ICDM '03 Proceedings of the Third IEEE International Conference on Data Mining
Maximizing the spread of influence through a social network
Proceedings of the ninth ACM SIGKDD international conference on Knowledge discovery and data mining
Friendster and publicly articulated social networking
CHI '04 Extended Abstracts on Human Factors in Computing Systems
SWIM: fostering social network based information search
CHI '04 Extended Abstracts on Human Factors in Computing Systems
Fast discovery of connection subgraphs
Proceedings of the tenth ACM SIGKDD international conference on Knowledge discovery and data mining
Exploring the community structure of newsgroups
Proceedings of the tenth ACM SIGKDD international conference on Knowledge discovery and data mining
Structure and evolution of blogspace
Communications of the ACM - The Blogosphere
CrimeNet explorer: a framework for criminal network knowledge discovery
ACM Transactions on Information Systems (TOIS)
Reality mining: sensing complex social systems
Personal and Ubiquitous Computing
A face(book) in the crowd: social Searching vs. social browsing
CSCW '06 Proceedings of the 2006 20th anniversary conference on Computer supported cooperative work
Co-occurrence prediction in a large location-based social network
Frontiers of Computer Science: Selected Publications from Chinese Universities
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Spatio-temporal data concerning the movement of individuals over space and time contains latent information on the associations among these individuals. Sources of spatio-temporal data include usage logs of mobile and Internet technologies. This article defines a spatio-temporal event by the co-occurrences among individuals that indicate potential associations among them. Each spatio-temporal event is assigned a weight based on the precision and uniqueness of the event. By aggregating the weights of events relating two individuals, we can determine the strength of association between them. We conduct extensive experimentation to investigate both the efficacy of the proposed model as well as the computational complexity of the proposed algorithms. Experimental results on three real-life spatio-temporal datasets cross-validate each other, lending greater confidence on the reliability of our proposed model.