Influential node ranking in social networks based on neighborhood diversity

Zareie, Ahmad; Sheikhahmadi, Amir; Jalili, Mahdi

doi:10.1016/j.future.2018.11.023

Cited by 73 publications

(39 citation statements)

References 30 publications

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“…These studies of influence maximization aim to discover nodes that can activate as many nodes as possible, which indicates that the influence of nodes can be propagated as extensively as possible. For example, Zareie et al [11] introduce two influential node ranking algorithms that use the diversity of the neighbors of each node to obtain its ranking value. Kumar & Panda [12] propose a coreness-based method to find influential nodes by voting.…”

Section: Structural Methodsmentioning

confidence: 99%

TPS: A Topological Potential Scheme to Predict Influential Network Nodes for Intelligent Communication in Social Networks

Mao

Zhou

Xiong

2021

IEEE Trans. Netw. Sci. Eng.

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Section: Structural Methodsmentioning

confidence: 99%

TPS: A Topological Potential Scheme to Predict Influential Network Nodes for Intelligent Communication in Social Networks

Mao

Zhou

Xiong

2021

IEEE Trans. Netw. Sci. Eng.

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“…These centralities have many classical measures such as the degree centrality (DC) [16], betweenness centrality (BC) [16], closeness centrality (CC) [5], and eigenvector centrality (EC) [5]. In addition to new measures such as the H-index centrality [44], those in optimal percolation theory [3] and evidence theory [15], the technique for order preference by similarity to the ideal solution (TOPSIS) [48], and other measures [47,31,46]. These centrality measures have been applied in various fields such as game theory [32], human cooperation [19], evolutionary games [20], relevant website ranking [49], and node synchronization [4,38].…”

Section: Introductionmentioning

confidence: 99%

Identification of influencers in complex networks by local information dimensionality

Wen

Deng

2020

Information Sciences

107

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The identification of influential spreaders in complex networks is a popular topic in studies of network characteristics. Many centrality measures have been proposed to address this problem, but most have limitations. In this paper, a method for identifying influencers in complex networks via the local information dimensionality is proposed. The proposed method considers the local structural properties around the central node; therefore, the scale of locality only increases to half of the maximum value of the shortest distance from the central node. Thus, the proposed method considers the quasilocal information and reduces the computational complexity. The information (number of nodes) in boxes is described via the Shannon entropy, which is more reasonable. A node is more influential when its local information dimensionality is higher. In order to show the effectiveness of the proposed method, five existing centrality measures are used as comparison methods to rank influential nodes in six real-world complex networks. In addition, a susceptible-infected (SI) model and Kendall's tau coefficient are applied to show the correlation between different methods. Experiment results show the superiority of the proposed method.

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“…Currently, many algorithms have been proposed for identifying influential nodes. Most of the identification algorithms are designed for online social networks [17][18][19]. In these identifications, some influence measures, such as degree centrality [11], betweenness centrality [20], closeness centrality [21], and Katz centrality [22], are highly dependent on the topological structure of the network.…”

Section: Introductionmentioning

confidence: 99%

A network-based method with privacy-preserving for identifying influential providers in large healthcare service systems

Mei

Cuomo

et al. 2020

Future Generation Computer Systems

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In data science, networks provide a useful abstraction of the structure of many complex systems, ranging from social systems and computer networks to biological networks and physical systems. Healthcare service systems are one of the main social systems that can also be understood using network-based approaches, for example, to identify and evaluate influential providers. In this paper, we propose a network-based method with privacy-preserving for identifying influential providers in large healthcare service systems. First, the provider-interacting network is constructed by employing publicly available information on locations and types of healthcare services of providers. Second, the ranking of nodes in the generated provider-interacting network is conducted in parallel on the basis of four nodal influence metrics. Third, the impact of the top-ranked influential nodes in the provider-interacting network is evaluated using three indicators. Compared with other research work based on patient-sharing networks, in this paper, the provider-interacting network of healthcare service providers can be roughly created according to the locations and the publicly available types of healthcare services, without the need for personally private electronic medical claims, thus protecting the privacy of patients. The proposed method is demonstrated by employing PhysicianandOtherSupplierDataCY2017, and can be applied to other similar datasets to help make decisions for the optimization of healthcare resources in the response to public health emergencies.

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Influential node ranking in social networks based on neighborhood diversity

Cited by 73 publications

References 30 publications

TPS: A Topological Potential Scheme to Predict Influential Network Nodes for Intelligent Communication in Social Networks

TPS: A Topological Potential Scheme to Predict Influential Network Nodes for Intelligent Communication in Social Networks

Identification of influencers in complex networks by local information dimensionality

A network-based method with privacy-preserving for identifying influential providers in large healthcare service systems

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