Identification of Node Influence Based on Improved K-shell Algorithm

Nodes that have greater influence in complex networks play an important role in controlling rumors propagation, optimizing resource allocation, spreading information efficiently, and advertising accurately. In view of the current many methods in identifying the node's influence had certain limitation, this paper based on the k-shell algorithm defined the concept of weighted degree, and putted forward the Modified K-shell Algorithm, shorted for MKS algorithm by measuring the potential importance of edges and considering the different contributions of neighbors. This algorithm considers the nodes' own features, location features and local features. Through implementing this algorithm on the representative Zachary karate club network and comparing with other typical methods, it is found that this algorithm improves the coarse division of k-shell algorithm, and its result is more reasonable.