An introduction to the special issue on cross-community mining

Abstract

People now live in heterogeneous social communities within cyber-physical spaces—both online communities (e.g., Flickr, Google?, LinkedIn) and social networks where digital content is exchanged, and opportunistic or offline communities that exploit opportunistic relationships between pairs of networked devices to exchange content (built on mobile ad hoc networking techniques) [1]. These communities have different technical features which lead to distinct kinds of interaction—such as patterns of comments and likes in online communities and co-location in offline communities, or issues of friendship, trust and influence in online communities and social popularity, and movement patterns in offline communities. We further envision the rapid development of cross-space communities in recent years, which try to bridge the gap between human interactions in the physical world and virtual world (by merging social elements in online social networks with physical contexts in offline communities). Significant examples include: location-based social networks (LBSNs, e.g., FourSquare, Jiepang) [2], which interlink online human interaction with offline check-ins; event-based social networks (EBSNs, e.g., MeetUp), which try to build the link between physical and online events [3]. Rather than viewing online communities and offline communities as competing, we see them as complementary and suggest cross-community mining (CCM), which aims to connect different forms of communities and study the interaction among them. In general, CCM emphasizes the interaction and interplay among different forms of communities, addressing the aggregation, association, and fusion of the multi-modal data extracted from cross-space, heterogeneous community environments.