Finding Influential Papers in Citation Networks

The citation network is the social network made up of papers and their citation relationships. A key task of the citation network is to find influential papers in the network. Traditional properties such as centralities can not reflect the influence of papers comprehensive, since it does not take the authority of the paper and the transfer effect into account. Other algorithms such as PageRank and HITS overcome those shortcomings. However, both of them involve matrix multiplication and repeated iterative process, which is less-effective. Comparing with another often mentioned network, the coauthor networks, we noticed that the citation network is a Directed Acyclic Graph(DAG) and it has a transitive relation. Making the most of properties of the citation network, we draw on the thought of topological sorting and design a more effective algorithm, whose time complexity is linear with the number of vertices and edges, which is O(V+E). In addition, we illustrate that the algorithm we proposed is stable and effective. We also apply our algorithm to another DAG task and results show that our algorithm has great scalability.