GMM: A generalized mechanics model for identifying the importance of nodes in complex networks Abstract   How to assess the importance of nodes in the network is an open question. There are many ways to identify the importance of nodes in complex networks. However, these methods have their own shortcomings and advantages. In particular, some methods based on the importance of nodes between interactions between nodes have been proposed. These methods utilize local information or path information. How to combine local and global information is still a problem. In this paper, a generalized mechanical model is proposed that uses global information and local information. To verify the effectiveness of the method, some experiments were performed on a total of ten real networks. In particular, an innovative experimental networkbased quality assessment was proposed to validate the method of identifying the importance of nodes.

Critical Nodes Identification in Complex Networks Critical nodes identification in complex networks is significance for studying the survivability and robustness of networks. The previous studies on structural hole theory uncovered that structural holes are gaps between a group of indirectly connected nodes and intermediaries that fill the holes and serve as brokers for information exchange. In this paper, we leverage the property of structural hole to design a heuristic algorithm based on local information of the network topology to identify node importance in undirected and unweighted network, whose adjacency matrix is symmetric. In the algorithm, a node with a larger degree and greater number of structural holes associated with it, achieves a higher importance ranking. Six real networks are used as test data. The experimental results show that the proposed method not only has low computational complexity, but also outperforms degree centrality, kshell method, mapping entropy centrality, the collective influence algorithm, DDN algorithm that based on node degree and their neighbors, and random ranking method in identifying node importance for network connectivity in complex networks.