Identifying influential nodes in weighted complex networks using an improved WVoteRank approach

Kumar, Sanjay; Panda, Ankit Kumar

doi:10.1007/s10489-021-02403-5

Cited by 29 publications

(6 citation statements)

References 38 publications

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“…In order to overcome this difficulty, Sun et al [32] extended VoteRank algorithm [33] to the weighted network and proposed the WVoteRank algorithm to find influential nodes. Subsequently, Kumar et al [34] improved WVoteRank by considering 2-hop neighbors. At the same time, Liu et al [35] developed the VoteRank++ algorithm by redefining voting mechanism in VoteRank algorithm.…”

Section: Methodsmentioning

confidence: 99%

“…Some well-known methods for identifying key nodes in weighted networks include weighted degree [41], weighted H-index [42], weighted coreness [43], and weighted betweenness [44]. We also compared HWVoteRank with weighted degree (w_degree), weighted H-index (w_hindex), and improved WVoteRank [34] (called PWVoteRank in this paper) that has excellent performance in identifying key nodes in a weighted network (by voting approach). As weighted coreness and weighted betweenness need to traverse all paths from each node to other nodes in the network, they have very high computational complexity and are not suitable for large biological information networks.…”

Section: Comparison Experimentsmentioning

confidence: 99%

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HWVoteRank: A Network-Based Voting Approach for Identifying Coding and Non-Coding Cancer Drivers

2022

Mathematics

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Cancer drivers play an important role in regulating cell growth, cell cycles, and DNA replication. Identifying these cancer drivers provides cancer researchers with indispensable knowledge that has important implications for clinical decision making. Some methods have been recently proposed to identify coding and non-coding cancer drivers through controllability analysis in network and eigenvector centrality based on community detection. However, the performance of these methods is not satisfactory. In this work, we focus on the strategy of selecting a set of critical nodes in cancer-special network as cancer drivers, and propose a novel approach for identifying coding and non-coding drives via a network-based voting mechanism. We name our approach HWVoteRank. Compared with two recent methods to identify cancer drivers, CBNA and NIBNA, and three algorithms for identifying key nodes on BRCA dataset, our method can achieve the best efficiency. By analyzing the results, it is found that our approach has better ability in identifying miRNA cancer drivers. We also applied our approach to identification of drivers of miRNA during Epithelial–Mesenchymal transition and drivers for cancer subtype. Through literature research, we found that those drivers explored by our approach are of biological significance.

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Section: Methodsmentioning

confidence: 99%

Section: Comparison Experimentsmentioning

confidence: 99%

HWVoteRank: A Network-Based Voting Approach for Identifying Coding and Non-Coding Cancer Drivers

2022

Mathematics

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“…The susceptible infected recovery (SIR) [41,42] model is a conventional infectious disease model as a descriptive information transfer. The SIR model consists of all nodes divided into three categories as follows: the susceptible, the infected, and the recovered nodes.…”

Section: Performance Metricsmentioning

confidence: 99%

A Novel Approach of Voterank-Based Knowledge Graph for Improvement of Multi-Attributes Influence Nodes on Social Networks

Pham

Duong

Tran

et al. 2023

Journal of Artificial Intelligence and Soft Computing Research

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Recently, measuring users and community influences on social media networks play significant roles in science and engineering. To address the problems, many researchers have investigated measuring users with these influences by dealing with huge data sets. However, it is hard to enhance the performances of these studies with multiple attributes together with these influences on social networks. This paper has presented a novel model for measuring users with these influences on a social network. In this model, the suggested algorithm combines Knowledge Graph and the learning techniques based on the vote rank mechanism to reflect user interaction activities on the social network. To validate the proposed method, the proposed method has been tested through homogeneous graph with the building knowledge graph based on user interactions together with influences in real-time. Experimental results of the proposed model using six open public data show that the proposed algorithm is an effectiveness in identifying influential nodes.

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“…Maximum Influence initially refers to the task of selecting 𝑘 seed nodes in social networks to maximize the transmission of the seed's influence [51,59,66,138]. It models viral marketing scenarios and can be applied to other scenarios like cascade monitoring and rumor control.…”

Section: Influence Maximization Problemmentioning

confidence: 99%

Epidemic Model-based Network Influential Node Ranking Methods: A Ranking Rationality Perspective

Zhang¹,

Zhao²,

Nie³

et al. 2023

Preprint

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Most recent surveys and reviews on Influential Node Ranking Methods (INRMs) hightlight discussions on the methods' technical details, but there still lacks in-depth research on the fundamental issue of how to verify the considerable influence of these nodes in a network. Compared to conventional verification models such as cascade failure and linear threshold, the epidemic model is more widely used. Accordingly, we conducted a survey of INRM based on epidemic model on 81 primary studies and analyzed their Capability and Correctness which we defined in our work. Our study categorized 4 types of networks used by INRM, classified 7 categories of INRMs for analyzing the networks and defined 2 evaluation metrics set of Capability and Correctness for evaluating INRM from Ranking Rationality Perspective. We also discussed particular real-world networks that were used to evaluate INRM and the Capability and Correctness of different INRMs on ranking nodes in specific networks. This is, as far as we know, the first survey aimed at systematically summarizing the Capability and Correctness of INRM. Our findings can assist practitioners and researchers in choosing and comparing INRMs and identifying research gaps.

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Identifying influential nodes in weighted complex networks using an improved WVoteRank approach

Cited by 29 publications

References 38 publications

HWVoteRank: A Network-Based Voting Approach for Identifying Coding and Non-Coding Cancer Drivers

HWVoteRank: A Network-Based Voting Approach for Identifying Coding and Non-Coding Cancer Drivers

A Novel Approach of Voterank-Based Knowledge Graph for Improvement of Multi-Attributes Influence Nodes on Social Networks

Epidemic Model-based Network Influential Node Ranking Methods: A Ranking Rationality Perspective

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