Identifying influential nodes in complex networks has attracted the attention of many researchers in recent years. However, due to the high time complexity, methods based on global attributes have become unsuitable for large-scale complex networks. In addition, compared with methods considering only a single attribute, considering multiple attributes can enhance the performance of the method used. Therefore, this paper proposes a new multiple local attributes-weighted centrality (LWC) based on information entropy, combining degree and clustering coefficient; both one-step and two-step neighborhood information are considered for evaluating the influence of nodes and identifying influential nodes in complex networks. Firstly, the influence of a node in a complex network is divided into direct influence and indirect influence. The degree and clustering coefficient are selected as direct influence measures. Secondly, based on the two direct influence measures, we define two indirect influence measures: two-hop degree and two-hop clustering coefficient. Then, the information entropy is used to weight the above four influence measures, and the LWC of each node is obtained by calculating the weighted sum of these measures. Finally, all the nodes are ranked based on the value of the LWC, and the influential nodes can be identified. The proposed LWC method is applied to identify influential nodes in four real-world networks and is compared with five well-known methods. The experimental results demonstrate the good performance of the proposed method on discrimination capability and accuracy.

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Identifying Influential Nodes in Complex Networks Based on Multiple Local Attributes and Information Entropy

Identifying influential nodes in complex networks is an essential research issue in network science since it may attribute to understand network structure and function. Majority of existing methods have been established by considering topological characteristics of networks. In this paper, we put forward a novel algorithm based on the Local Propagation Probability (LPP) model for identifying influential nodes in complex networks. The core idea of LPP algorithm is that the nodal influence is measured by total comprehensive scores of neighbor nodes within its three level neighborhood. Specially, the comprehensive score is calculated from three dimensions, namely the propagation influence score between different order neighbors, the propagation influence score in the same order neighbors and the hierarchical structure information of nodes. To validate the performance and applicability of the proposed algorithm, LPP is compared with eight state-of-the-art and competitive algorithms on nine real-world networks. Experimental results demonstrate that LPP performs better in terms of ranking accuracy, effectiveness, top-k nodes and distinguishing ability. The low time complexity allows LPP to be applied to large-scale sparse networks. 

A novel algorithm for identifying influential nodes in complex networks based on local propagation probability model

A mobile social network (MSN) is a mobile communications system that involves the social relationship of the users, in such a network, mobile users can spread information, opinions, ideas, rumors. Influence Maximization (IM) aims to identify <inline-formula><tex-math notation="LaTeX">$k$</tex-math></inline-formula> nodes from a network such that the influence spread generated by the <inline-formula><tex-math notation="LaTeX">$k$</tex-math></inline-formula> nodes is maximized, which has been attracting increasing attention in recent years. However, existing methods of influence maximization are heuristic algorithms based on network topology and greedy algorithms based on spreading. Accordingly, in this paper, we focused on Network Motifs (NM) as drivers of influence to impact the spreading process, we proposed IM-NM, a network motifs-based influence maximation scheme for delivering information efficiently. In consideration of the communication relationship and the users’ attributes, we first defined Weight Ratio (WR), Degree Density (DD), and Structural Stability Level (SSL). Then we identified the key network motifs by Naive Bayesian machine learning. Finally, we adopted the <inline-formula><tex-math notation="LaTeX">$k$</tex-math></inline-formula> key network motifs as the unit structure to reconstruct the network, and select the bridge node with strong communication ability in the key motifs to maximize the information. We implement our proposed methods on a set of real-world networks to evaluate the performance, the experimental results demonstrate that our proposal achieves better performance than other related methods.

Influence Maximization Based on Network Motifs in Mobile Social Networks

Assessing the importance of spreaders in networks is crucial for investigating the survival and robustness of networks. There are numerous potential applications, such as preventing outbreaks, spreading viruses on computer networks, viral marketing, and sickness spreading. These problems are usually unable to be solved by many heuristics with low time complexity. A number of tentative heuristics have been proposed for different application scenarios. However, we still lack an efficient heuristic to solve this type of problem effectively, for instance, low rating accuracy or high time complexity. To deal with this issue, this paper proposes a new heuristic called Locality-based Structure System (LSS), which is based on local information rather than global information of nodes in a network to determine the importance of spreaders. The proposed LSS takes into account the k-shell, degree, and number of triangles in a network. First, connectivity factors are computed based on the properties of nodes connected to them. Then, each node’s contribution to the importance of lines is computed. Finally, the degree and k-shell of nodes, as well as their contribution to the importance of lines, are taken into account. The proposed LSS is validated on a set of real and synthetic complex networks, where the simulation results under the standard SIR model and Kendall correlation coefficient indicate that LSS can efficiently identify influential spreaders in numerous types of networks without requiring any advanced parameter settings. 

LSS: A locality-based structure system to evaluate the spreader's importance in social complex networks

Information diffusion in social networks is a classic and, at the same time, very current problem. In fact, information diffusers are always looking for new techniques to disseminate information of their interest by creating backbones among them. In this paper, we focus on a specific, but very current and relevant, scenario regarding this way of proceeding. In fact, we propose an approach for the detection of possible backbones of information diffusers among different communities of a social network. Our approach is based on a new centrality measure that we call disseminator centrality. It is specifically designed to detect the so-called disseminator bridges, i.e., users belonging to multiple communities of a single social network, who want to disseminate information of their interest from one community to another by supporting each other. This paper describes the proposed approach, presents the disseminator centrality, illustrates the differences with respect to the related literature and presents the results of the experiments carried out to evaluate its performance. 

An approach to detect backbones of information diffusers among different communities of a social platform

Influential nodes selection to enhance data dissemination in mobile social networks: A survey

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Influential nodes selection to enhance data dissemination in mobile social networks: A survey