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The Solution to Node Importance in Complex Networks Based on PageRank Algorithm

Network science is an emerging interdiscipline that can solve practical issues by researching and analyzing properties of complex networks. In this paper, a modified algorithm based on PageRank is developed to construct the evaluation model in directed-weighted networks. This model can effectively determine the node importance in networks. Discussions about how to construct networks with the use of relationship between data and how to determine the weight of edges form the major concern of the present studies. Schemes for solving the above issues can differ in different networks. We successfully apply this model to citation, co-author and weibo follower networks. Through the results, we find that this optimized model can efficiently evaluate the importance of nodes in different complex networks and has strong applicability.