Sign prediction in social networks based on users reputation and optimism

Shahriari, Mohsen; Sichani, Omid Askari; Gharibshah, Joobin; Jalili, Mahdi

doi:10.1007/s13278-016-0401-6

Cited by 17 publications

(28 citation statements)

References 46 publications

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“…• All23+PI [10,27]: This is a variant of All23 [27] which considers four additional personality-based features (i.e., optimism and pessimism [10]) for each pair of users in addition to the existing 23 structural based features. • DB/OP/RP+PI [10,34]: This is a variant of DB/OP/RP [34] which uses the same four personality-based features [10] along with the existing topological-based features.…”

Section: Further Experiments With Personality Informationmentioning

confidence: 99%

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Signed Link Prediction with Sparse Data: The Role of Personality Information

Beigi

Ranganath

Liu

2019

Companion Proceedings of the 2019 World Wide Web Conference

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Predicting signed links in social networks often faces the problem of signed link data sparsity, i.e., only a small percentage of signed links are given. The problem is exacerbated when the number of negative links is much smaller than that of positive links. Boosting signed link prediction necessitates additional information to compensate for data sparsity. According to psychology theories, one rich source of such information is user's personality such as optimism and pessimism that can help determine her propensity in establishing positive and negative links. In this study, we investigate how personality information can be obtained, and if personality information can help alleviate the data sparsity problem for signed link prediction. We propose a novel signed link prediction model that enables empirical exploration of user personality via social media data. We evaluate our proposed model on two datasets of real-world signed link networks. The results demonstrate the complementary role of personality information in the signed link prediction problem. Experimental results also indicate the effectiveness of different levels of personality information for signed link data sparsity problem.

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Section: Further Experiments With Personality Informationmentioning

confidence: 99%

“…Exploiting user's features such as trustworthiness, bias, and optimism has been discussed in prior studies [30,34]. For example, the work of [34] addresses the problem of sign prediction based on users' optimism/reputation. The authors define optimism as users' voting pattern towards others and the reputation as their popularity.…”

Section: Related Workmentioning

confidence: 99%

Signed Link Prediction with Sparse Data: The Role of Personality Information

Beigi

Ranganath

Liu

2019

Companion Proceedings of the 2019 World Wide Web Conference

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“…To this end, [7] already introduced rank-based features named Optimism and Reputation to connect ranking problem with sign prediction. Rank-based reputation of node indicates patterns of voting toward this node.…”

Section: Edge Sign Predictionmentioning

confidence: 99%

“…If the users who vote toward node are high rank, then node can be considered reputable, but if they are not high rank we cannot say that node is reputable although the number of positive incoming links toward node is relatively high. The following equation can better describe rank-based reputation [7]:…”

Section: Edge Sign Predictionmentioning

confidence: 99%

“…In this paper, three community-based ranking algorithms for ranking of nodes have been proposed, and we have studied their impacts on the edge sign prediction problem. In order to study the impact of proposed ranking algorithms on signed prediction problem, we extracted the features of the predictor based on reputation and optimism introduced in [7]. Reputation of a node shows how much reputable a node is in the system and optimism denotes voting pattern of the node toward others.…”

Section: Introductionmentioning

confidence: 99%

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A Community-Based Approach for Link Prediction in Signed Social Networks

Shahriary

Shahriari

Noor

2015

Scientific Programming

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In signed social networks, relationships among nodes are of the types positive (friendship) and negative (hostility). One absorbing issue in signed social networks is predicting sign of edges among people who are members of these networks. Other than edge sign prediction, one can define importance of people or nodes in networks via ranking algorithms. There exist few ranking algorithms for signed graphs; also few studies have shown role of ranking in link prediction problem. Hence, we were motivated to investigate ranking algorithms availed for signed graphs and their effect on sign prediction problem. This paper makes the contribution of using community detection approach for ranking algorithms in signed graphs. Therefore, community detection which is another active area of research in social networks is also investigated in this paper. Community detection algorithms try to find groups of nodes in which they share common properties like similarity. We were able to devise three community-based ranking algorithms which are suitable for signed graphs, and also we evaluated these ranking algorithms via sign prediction problem. These ranking algorithms were tested on three large-scale datasets: Epinions, Slashdot, and Wikipedia. We indicated that, in some cases, these ranking algorithms outperform previous works because their prediction accuracies are better.

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