Employing Personality Feature to Rank the Influential Users in Signed Networks

Mohammadinejad, Amir; Farahbakhsh, Reza; Crespi, Noël

doi:10.1109/bdcloud-socialcom-sustaincom.2016.59

Cited by 7 publications

(8 citation statements)

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“…The same strategy is followed for the pessimist neighbor. We observed this valuable information of optimist and pessimist users will enhance the ranking performance [57]. In a signed network, the update rule of POPRank is computed for positive and negative subgraphs individually as below:…”

Section: ) the Poprank Methodologymentioning

confidence: 99%

“…In some researches, this algorithm is applied to the network of users considering the users and their connection instead of web pages and the hyperlinks. However, the algorithm does not verify from where these links are created (page's personality or behavior) and the singes of the link [57]. We considered all of the ranking algorithms and found that the PageRank is the most popular method to rank the users as a lot of new methods used it for their comparisons [57].…”

Section: ) the Pagerank Algorithmmentioning

confidence: 99%

“…However, the algorithm does not verify from where these links are created (page's personality or behavior) and the singes of the link [57]. We considered all of the ranking algorithms and found that the PageRank is the most popular method to rank the users as a lot of new methods used it for their comparisons [57]. According to that, in this manuscript, we take into account the PageRank as the body of our ranking method and modify it to consider the sings of the links and also we apply the personality measures to enhance its efficiency.…”

Section: ) the Pagerank Algorithmmentioning

confidence: 99%

“…In a network of users, pessimist users are the ones who have negative attitude about the faced problems and create a more negative connection (distrust link) rather than positive ones (trust link) to others. In contrast, the optimist users have positive attitude regarding the faced problems and make more positive connection [57]. Considering the list of products and the rates of users to them, we estimate each user's optimism and pessimism values based on the rates they expressed toward the products.…”

Section: ) Optimism and Pessimism Valuesmentioning

confidence: 99%

“…For instance, some of them contain social powers which means they possess more influence. In order to detect this power of the users in the network, we employed the ranking method [57] explained in III-A which ranks the users and gives a score to each of them. In other words, the users who are ranked higher (the ones who have more score), have more social power and subsequently have more impact on others.…”

Section: ) Users' Influence In Opinion Propagationmentioning

confidence: 99%

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Consensus Opinion Model in Online Social Networks Based on Influential Users

2019

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A framework to consensus opinion model within a networked social group is put forward. The current research in opinion formation within the groups is largely based on the opinion aggregation of each user of the network. However, the consistency of users in aggregation, social power, and the impact of each individual user of the group for opinion formation are not considered. In this paper, we investigate a consensus opinion model in social groups based on the impact of influential users and aggregation methods. In order to reach the consensus model, we aggregate the users' opinions. To maintain consistency, we propagate the opinion through the users to reach an agreement. This propagation will consider the influential users' impacts that have a crucial effect on its process. A novel method is proposed to detect the influential users and opinion propagation based on them to derive the opinion toward the networked social group. In particular, we applied optimism and pessimism scores as the users' personality to discover the influential users in the network. Considering that, we propagate the opinion based on two facts: 1) the impact of influential users, derived by the presence of an extremely confident individual in the network and 2) the impact of neighbors, induced by the presence of the users who have a connection with the current user. Then, we proposed the opinion aggregation of the group induced by the weighted averaging operator and fuzzy techniques. In order to evaluate the validity of the method, we used enormous data sets of Epinions and Etsy which are signed and unsigned, respectively. INDEX TERMS Influential users, link analysis, opinion propagation, voter model, fuzzy methods, consensus model, online social networks. I. INTRODUCTION The online social networks (OSN) are growing and penetrating the people lives such that almost every person in the world has a membership at least in one of them. A user may refer to the web to get information about TV, songs, movie tickets, jobs, or even mates. As these decisions and the fundamental financial processes move to the web, there is growing economic inspiration to propagate opinion through the web. Social networking websites have facilitated a new style of communication and opinion propagation through the links between their users. Today, a tremendous amount of users take part in different social networks such as eBay, Twitter, Amazon, Facebook, Alibaba etc. and create many social links with other users of that network. The networks of interest are online shopping centers in which the websites provide product for users. In order to recommend a product or prepare The associate editor coordinating the review of this manuscript and approving it for publication was Zhan Bu.

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Section: ) the Poprank Methodologymentioning

confidence: 99%

Section: ) the Pagerank Algorithmmentioning

confidence: 99%