Scalable Diversified Ranking on Large Graphs

Enhancing diversity in ranking on graphs has been identified as an important retrieval and mining task. Nevertheless, many existing diversified ranking algorithms cannot be scalable to large graphs as they have high time or space complexity. In this paper, we propose a scalable algorithm to find the top-K diversified ranking list on graphs. The key idea of our algorithm is that we first compute the Pagerank of the nodes of the graph, and then perform a carefully designed vertex selection algorithm to find the top-K diversified ranking list. Specifically, we firstly present a new diversified ranking measure, which can capture both relevance and diversity. Secondly, we prove the submodularity of the proposed measure. And then we propose an efficient greedy algorithm with linear time and space complexity with respect to the size of the graph to achieve near-optimal diversified ranking. Finally, we evaluate the proposed method through extensive experiments on four real networks. The experimental results indicate that the proposed method outperforms existing diversified ranking algorithms both on improving diversity in ranking and the efficiency of the algorithms.