Identifying Influential Nodes in Complex Networks Based on Neighborhood Entropy Centrality

Abstract

Identifying influential nodes is a fundamental and open issue in analysis of the complex networks. The measurement of the spreading capabilities of nodes is an attractive challenge in this field. Node centrality is one of the most popular methods used to identify the influential nodes, which includes the degree centrality (DC), betweenness centrality (BC) and closeness centrality (CC). The DC is an efficient method but not effective. The BC and CC are effective but not efficient. They have high computational complexity. To balance the effectiveness and efficiency, this paper proposes the neighborhood entropy centrality to rank the influential nodes. The proposed method uses the notion of entropy to improve the DC. For evaluating the performance, the susceptible-infected-recovered model is used to simulate the information spreading process of messages on nine real-world networks. The experimental results reveal the accuracy and efficiency of the proposed method.

Identifying and Ranking of Best Influential Spreaders With Extended Clustering Coefficient Local Global Centrality Method

“…This approach prevents selecting closely connected spreaders. In [29], authors proposed the trade-off between efficiency and effectiveness by using neighborhood entropy centrality, aiming to balance the shortcomings of degree centrality (DC), betweenness centrality (BC), and closeness centrality (CC). Jinfang shang et al [30] presents the GLS, a novel approach that combines the structural properties of the network.…”

Efficient Data Transfer by Evaluating Closeness Centrality for Dynamic Social Complex Network-Inspired Routing

“…The lower the above value, the closer a node is to the center of the network. For this reason, closeness is defined as the reciprocal of Equation (10), so that the more centered a node v is in the network, the higher its closeness metric is:…”

“…The search for the most influential or important nodes is a critical component of analyzing and comprehending the network topology dynamics due to its intrinsic role in determining the network's overall structure or efficiency [9][10][11]. Influential devices often act as key hubs for data exchange and efficient communication pathways and, depending on the objective, the significance of a node can vary.…”

Identifying Influential Spreaders in Complex Networks Using Neighborhood Network Structure

“…Global and local structure (GLS) [17]: It evaluates the influence of nodes by combining local information with global information. Neighborhood entropy centrality (NEC) [9]: It evaluates the node influence by calculating the sum of the entropies of the node's neighbors. RCNN [19]: It evaluates node influence by generating a neighborhood feature matrix for each node and applying a convolutional neural network.…”

“…In general, identifying influential spreaders algorithms can be roughly divided into two categories, including centralitybased algorithm and topology-based algorithm. Centralitybased algorithm also includes local centrality-based algorithms (LCAs) [3,4], global centrality-based algorithms (GCAs) [5,6], and semi-global centrality-based algorithms (SCAs) [7][8][9]. In general, centrality-based algorithms solely considering the features of nodes and neighbors will lead to ignoring differences in network position and structure, thereby affecting the accuracy and effectiveness of the algorithm.…”