Conference Proceeding Article
Summarizing patterns of connections or social tiesin a social network, in terms of attributes information on nodesand edges, holds a key to the understanding of how the actorsinteract and form relationships. We formalize this problem asmining top-k group relationships (GRs), which captures strongsocial ties between groups of actors. While existing works focuson patterns that follow from the well known homophily principle,we are interested in social ties that do not follow from homophily,thus, provide new insights. Finding top-k GRs faces new challenges:it requires a novel ranking metric because traditionalmetrics favor patterns that are expected from the homophilyprinciple; it requires an innovative search strategy since there isno obvious anti-monotonicity for such GRs; it requires a noveldata structure to avoid data explosion caused by multidimensionalnodes and edges and many-to-many relationships in a socialnetwork. We address these issues through presenting an efficientalgorithm, GRMiner, for mining top-k GRs and we evaluate itseffectiveness and efficiency using real data.
Databases and Information Systems
Data Management and Analytics
IEEE 32nd International Conference on Data Engineering (ICDE) 2016: May 16-20, 2016, Helsinki, Finland: Proceedings
City or Country
LIANG, Hongwei; WANG, Ke; and ZHU, Feida.
Mining social ties beyond homophily. (2016). IEEE 32nd International Conference on Data Engineering (ICDE) 2016: May 16-20, 2016, Helsinki, Finland: Proceedings. Research Collection School Of Information Systems.
Available at: http://ink.library.smu.edu.sg/sis_research/3569
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