Group formation in large social networks: membership, growth, and evolution
Proceedings of the 12th ACM SIGKDD international conference on Knowledge discovery and data mining
Finding a team of experts in social networks
Proceedings of the 15th ACM SIGKDD international conference on Knowledge discovery and data mining
Signed networks in social media
Proceedings of the SIGCHI Conference on Human Factors in Computing Systems
The community-search problem and how to plan a successful cocktail party
Proceedings of the 16th ACM SIGKDD international conference on Knowledge discovery and data mining
Power in unity: forming teams in large-scale community systems
CIKM '10 Proceedings of the 19th ACM international conference on Information and knowledge management
Team Formation for Generalized Tasks in Expertise Social Networks
SOCIALCOM '10 Proceedings of the 2010 IEEE Second International Conference on Social Computing
On social-temporal group query with acquaintance constraint
Proceedings of the VLDB Endowment
Discovering top-k teams of experts with/without a leader in social networks
Proceedings of the 20th ACM international conference on Information and knowledge management
The life and death of online groups: predicting group growth and longevity
Proceedings of the fifth ACM international conference on Web search and data mining
Incremental graph pattern matching
ACM Transactions on Database Systems (TODS)
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One important function of current social networking services is allowing users to initialize different kinds of activity groups (e.g. study group, cocktail party, and group buying) and invite friends to attend in either manual or collaborative manners. However, such process of group formation is tedious, and could either include inappropriate group members or miss relevant ones. This work proposes to automatically compose the activity groups in a social network according to user-specified activity information. Given the activity host, a set of labels representing the activity's subjects, the desired group size, and a set of must-inclusive persons, we aim to find a set of individuals as the activity group, in which members are required to not only be familiar with the host but also have great communications with each other. We devise an approximation algorithm to greedily solve the group composing problem. Experiments on a real social network show the promising effectiveness of the proposed approach as well as the satisfactory human subjective study.