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Collective Behavior of Social Networking Information by using Edge Clustering

It is well known that actors in a network demonstrate correlated behaviors. In this work, we aim to predict the outcome of collective behavior given a social network and the behavioral information of some actors. In particular, we explore scalable learning of collective behavior when millions of actors are involved in the network. Our approach follows a social-dimension based learning framework. Social dimensions are extracted to represent the potential affiliations of actors before discriminative learning occurs. As existing approaches to extract social dimensions suffer from scalability, it is imperative to address the scalability issue. We propose an edge-centric clustering scheme to extract social dimensions and a scalable k-means variant to handle edge clustering. Essentially, each edge is treated as one data instance, and the connected nodes are the corresponding features. Then, the proposed k-means clustering algorithm can be applied to partition the edges into disjoint sets, with each set representing one possible affiliation. With this edge-centric view, we show that the extracted social dimensions are guaranteed to be sparse. This model, based on the sparse social dimensions, shows comparable prediction performance with earlier social dimension approaches. An incomparable advantage of our model is that it easily scales to handle networks with millions of actors while the earlier models fail. This scalable approach offers a viable solution to effective learning of online collective behavior on large scale.