Abstract
The critical edges in complex networks are extraordinary edges which play more significant role than other edges on the structure and function of networks. The research on identifying critical edges in complex networks has attracted much attention because of its theoretical significance as well as wide range of applications. Considering the topological structure of networks and the ability to disseminate information, an edge ranking algorithm BCCMOD based on cliques and paths in networks is proposed in this report. The effectiveness of the proposed method is evaluated by SIR model, susceptibility index S and the size of giant component σ and compared with well-known existing metrics such as Jaccard coefficient, Bridgeness index, Betweenness centrality and Reachability index in nine real networks. Experimental results show that the proposed method outperforms these well-known methods in identifying critical edges both in network connectivity and spreading dynamic.
Highlights
The critical edges in complex networks are extraordinary edges which play more significant role than other edges on the structure and function of networks
In order to explore the transmission of information, many researches have focused on the network topology to find the critical edges
The proposed method is compared with Jaccard coefficient, Bridgeness, Betweenness centrality and Reachability index in three evaluation metrics, SIR model[24,25], susceptibility index S26 and the size of giant component σ27 in nine real networks which have large differences in basic topological features and the results show that the proposed method in this report can quickly decompose networks and has a greater impact on information spreading
Summary
The critical edges in complex networks are extraordinary edges which play more significant role than other edges on the structure and function of networks. The research on identifying critical edges in complex networks has attracted much attention because of its theoretical significance as well as wide range of applications. Considering the topological structure of networks and the ability to disseminate information, an edge ranking algorithm BCCMOD based on cliques and paths in networks is proposed in this report. Networks mediate the spread of information, sometimes, a few initial seeds can affect large portions of networks. We only use the topology of networks to rank the importance of edges, considering the local characteristics (degrees of nodes, cliques) and the global characteristics (betweenness centrality)
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