Authority-shift clustering: Hierarchical clustering by authority seeking on graphs

In this paper, a novel hierarchical clustering method using link analysis techniques is introduced. The algorithm is formulated as an authority seeking procedure on graphs, which computes the shifts toward nodes with high authority scores. For the authority shift, we adopted the personalized PageRank score of the graph. Based on the concept of authority seeking, we achieve hierarchical clustering by iteratively propagating the authority scores to other nodes and shifting authority nodes. This scheme solves the chicken-egg difficulty in hierarchical clustering by a semiglobal bottom-up approach exploiting the global structure of the graph. The experimental evaluation demonstrates that our algorithm is more powerful compared with existing graph-based approaches in clustering and image segmentation tasks.