A Novel Centrality of Influential Nodes Identification in Complex Networks

Abstract. Connecting points of interest through a well-planned, inter-connected network provides manifold benefits to commuters and service providers. In the South African context, traffic congestion has become of great concern. Given how the South Africa community is slowly developing towards the use of multi-modes of mobility, the Gautrain network can be used to promote the use of multi-modes of mobility, as the Gautrain has been identified as the backbone of mobility within the Gauteng province. Currently commuters have the option to board the Gaubus (a form of Bus Rapid Transit) at their origin points which will take them to the Gautrain station to board the Gautrain. The problem to be solved arises when a commuter wishes to traverse from any bus stop to the Gautrain station, currently he/she only has one option and if the bus network has a shutdown at any point in the network the commuter’s journey will not be possible. In solving this problem, we consider the problem of graph robustness (that is creating new alternative routes to increase node/bus stop connectivity). We initial use Strava data, to identify locations were cyclist prefer to cycle and at what time of day. In graph theory, the nodes with most spreading ability are called influential nodes. Identification of most influential nodes and ranking them based on their spreading ability is of vital importance. Closeness centrality and betweenness are one of the most commonly used methods to identify influential nodes in complex networks. Using the Gaubus network we identify the influential nodes/ bus stops, using the betweenness centrality measure. The results reveal the influential nodes with the highest connectivity as these have cross-connections in the network. Identification of the influential nodes presents an important implication for future planning, accessibility, and, more generally, quality of life.

A social network is a set of relationships and interactions among social entities such as individuals, organizations, and groups. The social network analysis is one of the major topics in the ongoing research field. The major problem regarding the social network is finding the most influential objects or persons. Identification of most influential nodes in a social network is a tedious task as large numbers of new users join the network every day. The most commonly used method is to consider the social network as a graph and find the most influential nodes by analyzing it. The degree centrality method is node based and has the advantage of easy identification of most influential nodes. In this paper a method called "Enhanced Degree Centrality Measure" is proposed which integrates clustering co-efficient value along with node based degree centrality. The enhanced degree centrality measure is applied to three different datasets which are obtained from the Facebook to analyze the performance. The response obtained is compared with existing methods such as degree centrality method and SPIN algorithm. By comparison, it is found that highest number of active nodes identified by the proposed method is 64 when compared with SPIN algorithm which identifies only 55.

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Ranking nodes in complex networks based on local structure and improving closeness centrality

CAGM: A communicability-based adaptive gravity model for influential nodes identification in complex networks

Identifying influential nodes in complex networks is essential for network robust and stability, such as viral marketing and information control. Various methods have been proposed to define the influence of nodes. In this paper, we comprehensively consider the global position and local structure to identify influential nodes. The number of iterations in the process of k-shell decomposition is taken into consideration, and the improved k-shell decomposition is then put forward. The improved k-shell decomposition and degree of target node are taken as the benchmark centrality, in addition, as is well known, the effect between node pairs is inversely proportional to the shortest path length between two nodes, and then we also consider the effect of neighbors on target node. To evaluate the performance of the proposed method, susceptible-infected (SI) model is adopted to simulate the spreading process in four real networks, and the experimental results show that the proposed method has obvious advantages over classical centrality measures in identifying influential nodes.

Influential nodes act as a hub for information transmission in a command and control network. The identification of influential nodes in a network of this nature is a significant and challenging task; however, it is necessary if the invulnerability of the network is to be increased. The existing k-shell method is problematic in that it features a coarse sorting granularity and does not consider the local centrality of nodes. Thus, the degree of accuracy with which the influential nodes can be identified is relatively low. This motivates us to propose a method based on an integral k-shell to identify the influential nodes in a command and control network. This new method takes both the global and local information of nodes into account, introduces the historical k-shell and a 2-order neighboring degree, and refines the k-shell decomposition process in a network. Simulation analysis is carried out from two perspectives: to determine the impact on network performance when influential nodes are removed and to obtain the correlation between the integral k-shell value and its propagation value. The simulation results show that the integral k-shell method, which employs an algorithm of lower complexity, accurately identifies the influence of those nodes with the same k-shell values. Furthermore, the method significantly improves the accuracy with which the influential nodes can be identified.

The identification of influential nodes in complex networks is a hot topic among scholars. Classical methods commonly analyze single node information or static structures but seldom emphasize dynamic properties and the interactive influence of nodes. Here, we proposed an improved Dynamic-Sensitive centrality (IDS) method by considering the interactive influence of both the self and neighbor nodes. Based on six real aviation networks and the Susceptible Infected Recovered (SIR) spreading disease model, we simulated the actual spreading process within these networks. Relevant experiments were conducted through Kendall’s correlation coefficient, the imprecision function, and the complementary cumulative distribution function. The experimental results demonstrated that the IDS can more accurately identify the influential node and effectively differentiate the node influence in the network compared with other benchmark methods. Especially in the EU air-2 network, the IDS results in Kendall’s correlation coefficient are improved by 105% compared to the DS centrality.

Influential nodes refer to the ability of a node to spread information in complex networks. Identifying influential nodes is an important problem in complex networks which plays a key role in many applications such as rumor controlling, virus spreading, viral market advertising, research paper views, and citations. Basic measures like degree centrality, betweenness centrality, closeness centrality are identifying influential nodes but they are incapable of largescale networks due to time complexity issues. Chen et al. [1] proposed semi-local centrality, which is reducing computation complexity and finding influential nodes in the network. Recently Yang et al. 2020 [2] proposed a novel centrality measure based on degree and clustering coefficient for identifying the influential nodes. Sanjay et al. 2020 [3] gave voterank and neighborhood coreness-based algorithms for finding the influenced nodes in the network. Zhiwei et al. 2019 [4] considered the average shortest path to discover the influenced node in the network. These are the few recent local,global and mixed centralities. In this paper, we show a broad view of recent methods for finding influential nodes in complex networks. It also analyzes the new challenges and limitations for a better understanding of each method in detail. The experimental results based on these methods show better performance compared with existing basic centrality measures.

Accurate identification of influential nodes facilitates the control of rumor propagation and interrupts the spread of computer viruses. Many classical approaches have been proposed by researchers regarding different aspects. To explore the impact of location information in depth, this paper proposes an improved global structure model to characterize the influence of nodes. The method considers both the node’s self-information and the role of the location information of neighboring nodes. First, degree centrality of each node is calculated, and then degree value of each node is used to represent self-influence, and degree values of the neighbor layer nodes are divided by the power of the path length, which is path attenuation used to represent global influence. Finally, an extended improved global structure model that considers the nearest neighbor information after combining self-influence and global influence is proposed to identify influential nodes. In this paper, the propagation process of a real network is obtained by simulation with the SIR model, and the effectiveness of the proposed method is verified from two aspects of discrimination and accuracy. The experimental results show that the proposed method is more accurate in identifying influential nodes than other comparative methods with multiple networks.

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A systematic survey on influential spreaders identification in complex networks with a focus on K-shell based techniques

Identification of influential nodes has emerged as one of the major challenges, especially after their use in the rapid propagation of information, epidemics, and so on in social media. Most of the previous works in this field deal with the homogeneous interactions that are not pertinent in the determination of the accurate context of the nodes due to their noisy and sparse nature. Hence, heterogeneous interactions need to be explored for the identification of influential nodes in the network. To consider heterogeneous interactions available within the network, a multilayer network (ML) has been designed in this work. Each layer of the network represents a particular type of interaction, e.g., upload, comment, retweet, reply, and mention. A heterogeneous degree ranking (HDR)-based influential nodes’ detection and ranking are proposed for the designed ML. Furthermore, multiple-criteria decision-making (MCDM) methods, such as the analytic hierarchy process (AHP), the technique for order of preference by similarity to ideal solution (TOPSIS), fuzzy AHP, fuzzy TOPSIS, and the analytic network process (ANP), are explored for the proposed ML for identification and ranking of the influential nodes. The susceptible–infected–recovered (SIR) model is used to evaluate the proposed work. In addition to this, statistical analysis is performed using the Pearson correlation, Kendall’s correlation, Spearman’s correlation, and the Friedman test on the ranks generated by different methods, which shows that the results generated by different proposed methods are consistent. Furthermore, the performance of the proposed method is compared with state-of-the-art approaches.

Fast ranking nodes importance in complex networks based on LS-SVM method