Identification of influential users in social networks based on users’ interest

Zareie, Ahmad; Sheikhahmadi, Amir; Jalili, Mahdi

doi:10.1016/j.ins.2019.04.033

Cited by 84 publications

(29 citation statements)

References 34 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…Advertising cost has also been taken into account, in addition to nodes influentiality, to determine influential users [25]- [26]. Zareie et al [2] measure the interest of users in marketing messages and then propose an algorithm to obtain the set of the most influential users in social networks. Weng et al [27] propose an extension of PageRank algorithm called Twitter-Rank, to measure the influence of users in Twitter.…”

Section: Hybrid Methodsmentioning

confidence: 99%

“…We also compare the performance of the proposed TPS with three existing methods for measuring node importance, namely, Weighted PageRank [49], Weighted HITS [50] and IMUD [2]. The top 20 influential nodes identified by the four different methods are shown in Table 6.…”

Section: Performance Evaluationmentioning

confidence: 99%

“…Although there have been a number of previous stud-ies about identifying or predicting influential nodes in OSNs [1]- [2], few have addressed the potential significance to brand communication or how to identify influential nodes that are more suitable for promoting brands through social media. Moreover, massive brand-related data have become a kind of big data [3] in OSNs.…”

Section: Introductionmentioning

confidence: 99%

See 2 more Smart Citations

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.

View full text Add to dashboard Cite

Section: Hybrid Methodsmentioning

confidence: 99%

Section: Performance Evaluationmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

See 1 more Smart Citation

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.

View full text Add to dashboard Cite

“…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

View full text Add to dashboard Cite

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.

show abstract

“…Hence, the identification of influential nodes in complex networks play an important role [11] in both structural and functional aspects [12,13], and is an important area of research [14]. The identification of influential nodes can be applied across various fields [15] such as disease [16], network system [17], biology [18], social system [19,20,4], time series [21], information propagation [22] and Parrondo's paradox [23,24,25,26,27]. Besides, identifying the vital nodes [28] can allow us to discover and address real-world problems [29,30] such as transportation hubs identifying, influence maximizing, rumor controlling [31], disease controlling [32], advertising and community finding [33,34].…”

Section: Introductionmentioning

confidence: 99%

Identifying Influential Nodes in Complex Networks Based on Local Effective Distance

et al. 2019

View full text Add to dashboard Cite

With the rapid development of Internet technology, the social network has gradually become an indispensable platform for users to release information, obtain information, and share information. Users are not only receivers of information, but also publishers and disseminators of information. How to select a certain number of users to use their influence to achieve the maximum dissemination of information has become a hot topic at home and abroad. Rapid and accurate identification of influential nodes in the network is of great practical significance, such as the rapid dissemination, suppression of social network information, and the smooth operation of the network. Therefore, from the perspective of improving computational accuracy and efficiency, we propose an influential node identification method based on effective distance, named KDEC. By quantifying the effective distance between nodes and combining the position of the node in the network and its local structure, the influence of the node in the network is obtained, which is used as an indicator to evaluate the influence of the node. Through experimental analysis of a lot of real-world networks, the results show that the method can quickly and accurately identify the influential nodes in the network, and is better than some classical algorithms and some recently proposed algorithms.

show abstract

Identification of influential users in social networks based on users’ interest

Cited by 84 publications

References 34 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

Identifying Influential Nodes in Complex Networks Based on Local Effective Distance

Contact Info

Product

Resources

About