Identifying Important Nodes in Complex Networks Based on Node Propagation Entropy

Abstract

In recent years, the identification of the essential nodes in complex networks has attracted significant attention because of their theoretical and practical significance in many applications, such as preventing and controlling epidemic diseases and discovering essential proteins. Several importance measures have been proposed from diverse perspectives to identify crucial nodes more accurately. In this paper, we propose a novel importance metric called node propagation entropy, which uses a combination of the clustering coefficients of nodes and the influence of the first- and second-order neighbor numbers on node importance to identify essential nodes from an entropy perspective while considering the local and global information of the network. Furthermore, the susceptible–infected–removed and susceptible–infected–removed–susceptible epidemic models along with the Kendall coefficient are used to reveal the relevant correlations among the various importance measures. The results of experiments conducted on several real networks from different domains show that the proposed metric is more accurate and stable in identifying significant nodes than many existing techniques, including degree centrality, betweenness centrality, closeness centrality, eigenvector centrality, and H-index.

Local-Forest Method for Superspreaders Identification in Online Social Networks

“…MC is the number of Monte Carlo experiments for each node. Related works in recent years usually set MC as 100 [ 32 , 33 , 34 , 53 ] or 1000 [ 23 , 31 , 39 , 52 ]. A relatively small MC (i.e., 100) is acceptable for large networks (i.e., with 10,000) [ 32 ].…”

“…Moreover, there are other centrality methods proposed continuously. The node propagation entropy measure [ 31 ] considers the neighbors within two hops and the clustering coefficients of nodes. The semi-global triangular centrality method [ 32 ] uses the triangular patterns around nodes to identify influential spreaders.…”

NNPEC: Neighborhood node propagation entropy centrality is a unique way to find the influential node in a complex network

“…It is possible to compare the amount of network degradation to the importance of a remote node. The node elimination method 31 and the node reduction approach are the two most commonly used techniques. Since each node contraction requires the determination of the average path length of a network, these strategies are unsuitable for networks with many nodes due to their high temporal complexity.…”

“…signifies the performance comparison of a proposed method under the karate dataset; (A-D) signifies the number of nodes, edges, AD, maximum degree, and CC, respectively. For karate, the dataset of the nodes with the actual values is 34, and the values obtained from the approaches LNC, MLAIE, NCICN, LSP, and proposed NNPEC are31,31,32,32,33, where the NNEC value is near to the actual value. The edges have an actual value is 78, and the values obtained from the approaches LNC, MLAIE, NCICN, LSP, and proposed NNPEC are 78, 72, 71, 74, 74, and 76, respectively, where the proposed NNPEC value is near to the actual value.…”

Complex networks analysis: centrality measures

“…For the first filtering constraint, the centrality is calculated using the user-defined values, and the user may then chose or choose the values of α and β. The user is in charge of the first level of filtering chains [9]. The user controls the first level of filtering chains, which is what const means [10].…”