Improved influential nodes identification in complex networks

Journal of Intelligent & Fuzzy Systems ◽

10.3233/jifs-202943 ◽

2021 ◽

pp. 1-9

Author(s):

Shi Dong ◽

Wengang Zhou

Keyword(s):

Theoretical Analysis ◽

Complex Networks ◽

Network Structure ◽

Information Entropy ◽

Network System ◽

Weighted Degree ◽

Identification Methods ◽

Hybrid Mechanism ◽

Influential Nodes ◽

Influential Node

Influential node identification plays an important role in optimizing network structure. Many measures and identification methods are proposed for this purpose. However, the current network system is more complex, the existing methods are difficult to deal with these networks. In this paper, several basic measures are introduced and discussed and we propose an improved influential nodes identification method that adopts the hybrid mechanism of information entropy and weighted degree of edge to improve the accuracy of identification (Hm-shell). Our proposed method is evaluated by comparing with nine algorithms in nine datasets. Theoretical analysis and experimental results on real datasets show that our method outperforms other methods on performance.

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A dynamic weighted TOPSIS method for identifying influential nodes in complex networks

Modern Physics Letters B ◽

10.1142/s0217984918502160 ◽

2018 ◽

Vol 32 (19) ◽

pp. 1850216 ◽

Cited By ~ 4

Author(s):

Pingle Yang ◽

Xin Liu ◽

Guiqiong Xu

Keyword(s):

Complex Networks ◽

Centrality Measures ◽

Topsis Method ◽

Decision Making Problem ◽

Significant Research ◽

Order Preference ◽

Multi Attribute Decision Making ◽

Influential Nodes ◽

Influential Node ◽

Grey Relational

Identifying the influential nodes in complex networks is a challenging and significant research topic. Though various centrality measures of complex networks have been developed for addressing the problem, they all have some disadvantages and limitations. To make use of the advantages of different centrality measures, one can regard influential node identification as a multi-attribute decision-making problem. In this paper, a dynamic weighted Technique for Order Preference by Similarity to Ideal Solution (TOPSIS) is developed. The key idea is to assign the appropriate weight to each attribute dynamically, based on the grey relational analysis method and the Susceptible–Infected–Recovered (SIR) model. The effectiveness of the proposed method is demonstrated by applications to three actual networks, which indicates that our method has better performance than single indicator methods and the original weighted TOPSIS method.

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Identifying Influential Nodes of Complex Networks Based on Trust-Value

Algorithms ◽

10.3390/a13110280 ◽

2020 ◽

Vol 13 (11) ◽

pp. 280

Author(s):

Jinfang Sheng ◽

Jiafu Zhu ◽

Yayun Wang ◽

Bin Wang ◽

Zheng’ang Hou

Keyword(s):

Complex Networks ◽

Complex Network ◽

Real World ◽

Trust Value ◽

The Real ◽

The World ◽

Attribute Information ◽

Similarity Ratio ◽

Influential Nodes ◽

Influential Node

The real world contains many kinds of complex network. Using influence nodes in complex networks can promote or inhibit the spread of information. Identifying influential nodes has become a hot topic around the world. Most of the existing algorithms used for influential node identification are based on the structure of the network such as the degree of the nodes. However, the attribute information of nodes also affects the ranking of nodes’ influence. In this paper, we consider both the attribute information between nodes and the structure of networks. Therefore, the similarity ratio, based on attribute information, and the degree ratio, based on structure derived from trust-value, are proposed. The trust–PageRank (TPR) algorithm is proposed to identify influential nodes in complex networks. Finally, several real networks from different fields are selected for experiments. Compared with some existing algorithms, the results suggest that TPR more rationally and effectively identifies the influential nodes in networks.

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Hunting for Influential Nodes in Complex Networks Using Local Information

10.21203/rs.3.rs-145317/v1 ◽

2021 ◽

Author(s):

Zhihao Dong ◽

Yuanzhu Chen ◽

Terrence S. Tricco ◽

Cheng Li ◽

Ting Hu

Keyword(s):

Complex Networks ◽

Real World ◽

Structure And Function ◽

Epidemic Threshold ◽

Global Knowledge ◽

The Real ◽

Influential Nodes ◽

Influential Node ◽

And Function ◽

High Degree

Abstract Complex networks in the real world are often with heterogeneous degree distributions. The structure and function of nodes can vary significantly, with influential nodes playing a crucial role in information spread and other spreading phenomena. Identifying high-degree nodes enables change to the network’s structure and function. Previous work either redefines metrics used to measure the nodes’ importance or focus on developing algorithms to efficiently find influential nodes. These approaches typically rely on global knowledge of the network and assume that the structure of the network does not change over time, both of which are difficult to achieve in the real world. In this paper, we propose a decentralized strategy that can find influential nodes without global knowledge of the network. Our Joint Nomination (JN) strategy selects a random set of nodes along with a set of nodes connected to those nodes, and together they nominate the influential node set. Experiments are conducted on 12 network datasets, including both synthetic and real-world networks, both undirected and directed networks. Results show that average degree of the identified node set is about 3–8 times higher than that of the full node set, and the degree distribution skews toward higher-degree nodes. Removal of influential nodes increase the average shortest path length by 20–70% over the original network, or about 8–15% longer than the other decentralized strategies. Immunization based on JN is more efficient than other strategies, consuming around 12–40% less immunization resources to raise the epidemic threshold to 𝜏 ~ 0:1. Susceptible-Infected-Recovered (SIR) simulations on networks with 30% influential nodes removed using JN delays the arrival time of infection peak significantly and reduce the total infection scale to 15%.

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Influential Nodes Identification in Complex Networks via Information Entropy

Entropy ◽

10.3390/e22020242 ◽

2020 ◽

Vol 22 (2) ◽

pp. 242 ◽

Cited By ~ 9

Author(s):

Chungu Guo ◽

Liangwei Yang ◽

Xiao Chen ◽

Duanbing Chen ◽

Hui Gao ◽

...

Keyword(s):

Complex Networks ◽

Simulation Model ◽

Information Entropy ◽

Attenuation Factor ◽

Scientific Publications ◽

Dynamic Update ◽

Influential Nodes ◽

Update Strategy ◽

Specific Number ◽

Important Nodes

Identifying a set of influential nodes is an important topic in complex networks which plays a crucial role in many applications, such as market advertising, rumor controlling, and predicting valuable scientific publications. In regard to this, researchers have developed algorithms from simple degree methods to all kinds of sophisticated approaches. However, a more robust and practical algorithm is required for the task. In this paper, we propose the EnRenew algorithm aimed to identify a set of influential nodes via information entropy. Firstly, the information entropy of each node is calculated as initial spreading ability. Then, select the node with the largest information entropy and renovate its l-length reachable nodes’ spreading ability by an attenuation factor, repeat this process until specific number of influential nodes are selected. Compared with the best state-of-the-art benchmark methods, the performance of proposed algorithm improved by 21.1%, 7.0%, 30.0%, 5.0%, 2.5%, and 9.0% in final affected scale on CEnew, Email, Hamster, Router, Condmat, and Amazon network, respectively, under the Susceptible-Infected-Recovered (SIR) simulation model. The proposed algorithm measures the importance of nodes based on information entropy and selects a group of important nodes through dynamic update strategy. The impressive results on the SIR simulation model shed light on new method of node mining in complex networks for information spreading and epidemic prevention.

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Identification of nodes influence based on global structure model in complex networks

Scientific Reports ◽

10.1038/s41598-021-84684-x ◽

2021 ◽

Vol 11 (1) ◽

Author(s):

Aman Ullah ◽

Bin Wang ◽

JinFang Sheng ◽

Jun Long ◽

Nasrullah Khan ◽

...

Keyword(s):

Complex Networks ◽

Disease Transmission ◽

Closeness Centrality ◽

Global Structure ◽

Structure Model ◽

Relevant Research ◽

H Index ◽

Influential Nodes ◽

Influential Node ◽

Global Influence

AbstractIdentification of Influential nodes in complex networks is challenging due to the largely scaled data and network sizes, and frequently changing behaviors of the current topologies. Various application scenarios like disease transmission and immunization, software virus infection and disinfection, increased product exposure and rumor suppression, etc., are applicable domains in the corresponding networks where identification of influential nodes is crucial. Though a lot of approaches are proposed to address the challenges, most of the relevant research concentrates only on single and limited aspects of the problem. Therefore, we propose Global Structure Model (GSM) for influential nodes identification that considers self-influence as well as emphasizes on global influence of the node in the network. We applied GSM and utilized Susceptible Infected Recovered model to evaluate its efficiency. Moreover, various standard algorithms such as Betweenness Centrality, Profit Leader, H-Index, Closeness Centrality, Hyperlink Induced Topic Search, Improved K-shell Hybrid, Density Centrality, Extended Cluster Coefficient Ranking Measure, and Gravity Index Centrality are employed as baseline benchmarks to evaluate the performance of GSM. Similarly, we used seven real-world and two synthetic multi-typed complex networks along-with different well-known datasets for experiments. Results analysis indicates that GSM outperformed the baseline algorithms in identification of influential node(s).

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Comprehensive influence of topological location and neighbor information on identifying influential nodes in complex networks

PLoS ONE ◽

10.1371/journal.pone.0251208 ◽

2021 ◽

Vol 16 (5) ◽

pp. e0251208

Author(s):

Xiaohua Wang ◽

Qing Yang ◽

Meizhen Liu ◽

Xiaojian Ma

Keyword(s):

Complex Networks ◽

Network Structure ◽

Hybrid Method ◽

Spreading Process ◽

Position Information ◽

The Real ◽

Contact Distance ◽

Influential Nodes ◽

Highly Correlated ◽

Influential Individuals

Identifying the influential nodes of complex networks is now seen as essential for optimizing the network structure or efficiently disseminating information through networks. Most of the available methods determine the spreading capability of nodes based on their topological locations or the neighbor information, the degree of node is usually used to denote the neighbor information, and the k-shell is used to denote the locations of nodes, However, k-shell does not provide enough information about the topological connections and position information of the nodes. In this work, a new hybrid method is proposed to identify highly influential spreaders by not only considering the topological location of the node but also the neighbor information. The percentage of triangle structures is employed to measure both the connections among the neighbor nodes and the location of nodes, the contact distance is also taken into consideration to distinguish the interaction influence by different step neighbors. The comparison between our proposed method and some well-known centralities indicates that the proposed measure is more highly correlated with the real spreading process, Furthermore, another comprehensive experiment shows that the top nodes removed according to the proposed method are relatively quick to destroy the network than other compared semi-local measures. Our results may provide further insights into identifying influential individuals according to the structure of the networks.

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Spreading the information in complex networks: Identifying a set of top-N influential nodes using network structure

Decision Support Systems ◽

10.1016/j.dss.2021.113608 ◽

2021 ◽

pp. 113608

Author(s):

Mukul Gupta ◽

Rajhans Mishra

Keyword(s):

Complex Networks ◽

Network Structure ◽

Influential Nodes

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Modelling community structure and temporal spreading on complex networks

Computational Social Networks ◽

10.1186/s40649-021-00094-z ◽

2021 ◽

Vol 8 (1) ◽

Author(s):

Vesa Kuikka

Keyword(s):

Community Structure ◽

Complex Networks ◽

Community Detection ◽

Network Structure ◽

Network Connectivity ◽

Network Models ◽

Building Blocks ◽

Detection Algorithm ◽

Research Area ◽

Detection Methods

AbstractWe present methods for analysing hierarchical and overlapping community structure and spreading phenomena on complex networks. Different models can be developed for describing static connectivity or dynamical processes on a network topology. In this study, classical network connectivity and influence spreading models are used as examples for network models. Analysis of results is based on a probability matrix describing interactions between all pairs of nodes in the network. One popular research area has been detecting communities and their structure in complex networks. The community detection method of this study is based on optimising a quality function calculated from the probability matrix. The same method is proposed for detecting underlying groups of nodes that are building blocks of different sub-communities in the network structure. We present different quantitative measures for comparing and ranking solutions of the community detection algorithm. These measures describe properties of sub-communities: strength of a community, probability of formation and robustness of composition. The main contribution of this study is proposing a common methodology for analysing network structure and dynamics on complex networks. We illustrate the community detection methods with two small network topologies. In the case of network spreading models, time development of spreading in the network can be studied. Two different temporal spreading distributions demonstrate the methods with three real-world social networks of different sizes. The Poisson distribution describes a random response time and the e-mail forwarding distribution describes a process of receiving and forwarding messages.

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