Clustering Activation Networks

A real-world graph often has frequently interacting nodes on less frequently updated edges. Each interaction activates an existing edge and changes the activeness of the edge. In such an activation network, nodes that are cohesively connected by active edges form a cluster in both structural and temporal senses. For activation networks, incrementally maintaining a structure for an efficient clustering query processing is thus important. This raises problems on maintaining the edge activeness, combining the structural cohesiveness and activeness for clustering, and designing indexes for online clustering queries. This paper considers the time-decay scheme in modelling the activeness and proposes a suite of techniques with great effort made on simplification and innovation for efficiency, effectiveness and scalability. The query time is only related to the query results as opposed to the graph. The index size is linear up to a logarithmic factor. Extensive experiments verify the quality of the clustering results and moreover, the update time is up to six orders of magnitude faster than the baseline.