Social influence modeling using information theory in mobile social networks

Peng, Sancheng; Yang, Aimin; Cao, Lihong; Yu, Shui; Xie, Dongqing

doi:10.1016/j.ins.2016.08.023

Cited by 174 publications

(81 citation statements)

References 13 publications

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“…Fei and Deng [49] addressed the problem of how to identify influential nodes in complex networks by using relative entropy and the TOPSIS method, which combines the advantages of existing centrality measures and demonstrated the effectiveness of the proposed method based on experimental results. Peng et al [50] characterized the features of mobile social networks and presented an evaluation model to quantify influence by analyzing and calculating the friend entropy and communication frequency entropy between users to depict the uncertainty and complexity of social influence.…”

Section: Literature Reviewmentioning

confidence: 99%

How to Identify the Most Powerful Node in Complex Networks? A Novel Entropy Centrality Approach

Qiao

Shan

Zhou

2017

Entropy

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Centrality is one of the most studied concepts in network analysis. Despite an abundance of methods for measuring centrality in social networks has been proposed, each approach exclusively characterizes limited parts of what it implies for an actor to be "vital" to the network. In this paper, a novel mechanism is proposed to quantitatively measure centrality using the re-defined entropy centrality model, which is based on decompositions of a graph into subgraphs and analysis on the entropy of neighbor nodes. By design, the re-defined entropy centrality which describes associations among node pairs and captures the process of influence propagation can be interpreted explained as a measure of actor potential for communication activity. We evaluate the efficiency of the proposed model by using four real-world datasets with varied sizes and densities and three artificial networks constructed by models including Barabasi-Albert, Erdos-Renyi and Watts-Stroggatz. The four datasets are Zachary's karate club, USAir97, Collaboration network and Email network URV respectively. Extensive experimental results prove the effectiveness of the proposed method.

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Section: Literature Reviewmentioning

confidence: 99%

How to Identify the Most Powerful Node in Complex Networks? A Novel Entropy Centrality Approach

Qiao

Shan

Zhou

2017

Entropy

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“…Moreover, we propose a slope one algorithm based on the fusion of trusted data and user similarity. The algorithm we proposed can applyed in many applications, such as the recommendation system for social networks (Peng et al 2017a;Cai et al 2017;Jiang et al 2016), or loaction-based services (Peng et al 2017b).…”

Section: Resultsmentioning

confidence: 99%

A trust-based collaborative filtering algorithm for E-commerce recommendation system

Jiang

Cheng

Yang

et al. 2018

J Ambient Intell Human Comput

182

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The rise of e-commerce has not only given consumers more choice but has also caused information overload. In order to quickly find favorite items from vast resources, users are eager for technology by which websites can automatically deliver items in which they may be interested. Thus, recommender systems are created and developed to automate the recommendation process. In the field of collaborative filtering recommendations, the accuracy requirement of the recommendation algorithm always makes it complex and difficult to implement one algorithm. The slope one algorithm is not only easy to implement but also works efficient and effective. However, the prediction accuracy of the slope one algorithm is not very high. Moreover, the slope one algorithm does not perform so well when dealing with personalized recommendation tasks that concern the relationship among users. To solve these problems, we propose a slope one algorithm based on the fusion of trusted data and user similarity, which can be deployed in various recommender systems. This algorithm comprises three procedures. First, we should select trusted data. Second, we should calculate the similarity between users. Third, we need to add this similarity to the weight factor of the improved slope one algorithm, and then, we get the final recommendation equation. We have carried out a number of experiments with the Amazon dataset, and the results prove that our recommender algorithm performs more accurately than the traditional slope one algorithm.

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“…Our approach is also related to the concept of information diffusion in OSNs, in the sense that the roots influence the trust values of other nodes in the network following the rules of OSN information flow [15], [16]. Most of these works study how information flows in OSNs and propose strategies to maximize this diffusion by identifying strategical nodes for the information propagation.…”

Section: Related Workmentioning

confidence: 99%

Contrasting False Identities in Social Networks by Trust Chains and Biometric Reinforcement

Buccafurri

Lax

Migdal

et al. 2017

2017 International Conference on Cyberworlds (CW)

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Abstract-Fake identities and identity theft are issues whose relevance is increasing in the social network domain. This paper deals with this problem by proposing an innovative approach which combines a collaborative mechanism implementing a trust graph with keystroke-dynamic-recognition techniques to trust identities. The trust of each node is computed on the basis of neighborhood recognition and behavioral biometric support. The model leverages the word of mouth propagation and a settable degree of redundancy to obtain robustness. Experimental results show the benefit of the proposed solution even if attack nodes are present in the social network.

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Social influence modeling using information theory in mobile social networks

Cited by 174 publications

References 13 publications

How to Identify the Most Powerful Node in Complex Networks? A Novel Entropy Centrality Approach

How to Identify the Most Powerful Node in Complex Networks? A Novel Entropy Centrality Approach

A trust-based collaborative filtering algorithm for E-commerce recommendation system

Contrasting False Identities in Social Networks by Trust Chains and Biometric Reinforcement

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