Identifying Node Importance by Combining Betweenness Centrality and Katz Centrality

The assessment of node importance has been a fundamental issue in the research of complex networks, which is of overreaching importance to improve the robustness of actual system and design efficient system structure. Most researchers use the betweenness centrality (BC) or Katz centrality (KC) to measure node importance. However, the betweenness only takes into account the shortest path, regardless of the non-shortest path. The Katz centrality gives different weights to all the paths in the network, but the ranking result is close to the result of local path index. Therefore, a new algorithm combines betweenness centrality and Katz centrality(BKC) is proposed, which considers both the local node importance and the global node importance comprehensively, and overcomes the limitations that the node importance evaluation only depending on the adjacent nodes. Experimental results on the kite network and the APRP network illustrate that BKC is more effective to find out important nodes in different types of complex networks than the algorithms compared. Moreover, the cascading failures on the author collaboration network also illustrate that the networks are more vulnerable when continuously removing the important nodes identified by BKC, which further proves the effectiveness of our method.