A Novel Method to Rank Influential Nodes in Complex Networks Based on Tsallis Entropy

Xue-gong, Chen; Zhou, Jie; Liao, Zhifang; Liu, Shengzong; Zhang, Yan

doi:10.3390/e22080848

Cited by 17 publications

(19 citation statements)

References 32 publications

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“…Sun and Ng [ 33 ] use graph entropy based upon the centrality of users to measure the influence of connectors on social networks. Chen et al [ 34 ] consider network topology and proposed a method to rank the influential nodes by considering the Tsallis entropy of the users and their neighbors. Transfer entropy is another entropy-based measurement that is used to quantify influence.…”

Section: Background and Related Workmentioning

confidence: 99%

Influence Cascades: Entropy-Based Characterization of Behavioral Influence Patterns in Social Media

Senevirathna

Gunaratne

Rand

et al. 2021

Entropy

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Influence cascades are typically analyzed using a single metric approach, i.e., all influence is measured using one number. However, social influence is not monolithic; different users exercise different influences in different ways, and influence is correlated with the user and content-specific attributes. One such attribute could be whether the action is an initiation of a new post, a contribution to a post, or a sharing of an existing post. In this paper, we present a novel method for tracking these influence relationships over time, which we call influence cascades, and present a visualization technique to better understand these cascades. We investigate these influence patterns within and across online social media platforms using empirical data and comparing to a scale-free network as a null model. Our results show that characteristics of influence cascades and patterns of influence are, in fact, affected by the platform and the community of the users.

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Section: Background and Related Workmentioning

confidence: 99%

Influence Cascades: Entropy-Based Characterization of Behavioral Influence Patterns in Social Media

Senevirathna

Gunaratne

Rand

et al. 2021

Entropy

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“…Until now, great endeavors have been made to addressing the influence maximization problem in multilayer networks [35][36][37][38][39]. Wang et al [12] propose a centrality measure (EDCPTD) to identify essential nodes based on tensor decomposition, which incorporates the intralayer edges and interlayer edges simultaneously to measure the node importance in multilayer networks.…”

Section: Influence Maximization In Multilayer Networkmentioning

confidence: 99%

MINE: Identifying Top-k Vital Nodes in Complex Networks via Maximum Influential Neighbors Expansion

et al. 2020

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Identifying vital nodes in complex networks is of paramount importance in understanding and controlling the spreading dynamics. Currently, this study is facing great challenges in dealing with big data in many real-life applications. With the deepening of the research, scholars began to realize that the analysis on traditional graph model is insufficient because many nodes in a multilayer network share connections among different layers. To address this problem both efficiently and effectively, a novel algorithm for identifying vital nodes in both monolayer and multilayer networks is proposed in this paper. Firstly, a node influence measure is employed to determine the initial leader of a local community. Subsequently, the community structures are revealed via the Maximum Influential Neighbors Expansion (MINE) strategy. Afterward, the communities are regarded as super-nodes for an iteratively folding process till convergence, in order to identify influencers hierarchically. Numerical experiments on 32 real-world datasets are conducted to verify the performance of the proposed algorithm, which shows superiority to the competitors. Furthermore, we apply the proposed algorithm in the graph of adjacencies derived from the maps of China and USA. The comparison and analysis of the identified provinces (or states) suggest that the proposed algorithm is feasible and reasonable on real-life applications.

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“…Influence can only spread if it is transmitted through a social network (SN) 7,8 . There are two types of SNs: fixed and dynamic.…”

Section: Introductionmentioning

confidence: 99%

“…Influence can only spread if it is transmitted through a social network (SN). 7,8 There are two types of SNs: fixed and dynamic. In fixed networks, determining prominent nodes is fairly straightforward, and various algorithms are imposed for identifying these nodes.…”

mentioning

confidence: 99%

NNPEC: Neighborhood node propagation entropy centrality is a unique way to find the influential node in a complex network

Chakravarthy

Lokesh

2023

Concurrency and Computation

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Summary In complicated networks, it is still difficult to determine which nodes are influential. To increase the accuracy for identifying influential nodes (IN), several measures have been undertaken in complex networks (CN). Reviewing the research on network attacks, information distribution, and pandemic spread require identifying effective INs. As a result, determining INs in complicated networks has attracted more and more attention toward researchers. Various approaches have been proposed in recent years, each with pros and cons. The development of algorithms to study static social networks has been the focus of previous research. However, social networks are dynamic networks that change over time and are not static. The impact of source localization is generally weak since most existing approaches do not fully exploit the infection of neighboring information in nodes. This research proposes a novel Neighborhood Node Propagation Entropy Centrality (NNPEC) technique for finding the INs in CNs. This technique also helps to identify the neighborhood node entropy and determines each node's ability to spread among uninfected neighboring nodes. The proposed strategy is applied to six different datasets from different domains, including C‐Elegance, Dolphins, Facebook, Gowalla, Karate, and USA Power Grid. Some of the performance evaluations, like maximum degree (MD), average degree (AD), number of nodes, edges and cluster coefficient (CC), are analyzed and compared with different existing algorithms. In the experimental section, a proposed method obtained an MD of 16.66, CC of 0.564, AD of 3.92, edges of 76 and nodes of 33 for the karate dataset. Also, some other existing techniques are compared to prove the efficiency of the proposed method.

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A Novel Method to Rank Influential Nodes in Complex Networks Based on Tsallis Entropy

Cited by 17 publications

References 32 publications

Influence Cascades: Entropy-Based Characterization of Behavioral Influence Patterns in Social Media

Influence Cascades: Entropy-Based Characterization of Behavioral Influence Patterns in Social Media

MINE: Identifying Top-k Vital Nodes in Complex Networks via Maximum Influential Neighbors Expansion

NNPEC: Neighborhood node propagation entropy centrality is a unique way to find the influential node in a complex network

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