Roles in social networks: Methodologies and research issues

Forestier, Mathilde; Stavrianou, Anna; Velcin, Julien; Zighed, Djamel A.

doi:10.3233/wia-2012-0236

Cited by 30 publications

(12 citation statements)

References 58 publications

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“…The concept of an expert is generally defined as: in a social network, with relevant knowledge about the topic in question, and his opinions and ideas are credible. We will call such a person an expert [3]. Content-based expert role identification methods are mostly based on the topic model.…”

Section: Expert Rolementioning

confidence: 99%

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High-Value User Identification Based on Topic Weight

et al. 2019

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The role identification in social networks is important for analyzing and understanding social networks, predicting user behavior, and researching relationships and interactions between users. Most role identification works are based on link analysis following the ideas of PageRank and HITS, and some methods also combing link analysis and content analysis. However, previous role identification methods focus on the amount of topic released by the target user without considering the importance of the topic. In order to identify high-value users by topic weights in social networks, this paper first proposes a method of assigning topic weights based on the LDA model and the collective credit allocation method in science(CCA). Then we introduce content value density based on the Kullback-Leibler divergence of topic weight and topic distribution to rank the high-value users. In addition, the new method is more suitable for calculation on large-scale networks and time-evolution networks because the new method is an inductive method which works on the local graph. Finally, the experiments are carried out to analyze the effect of the topic weight allocation and content value density in a real data set. The results show that the new method is superior to the compared methods when identifying high-value users in certain circumstances. INDEX TERMS Social network, role identification, high-value user, topic weight.

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Section: Expert Rolementioning

confidence: 99%

“…whose opinions and ideas are credible [3]. Most role identification methods of the social network often contain two parts: link analysis and content analysis.…”

Section: Introductionmentioning

confidence: 99%

High-Value User Identification Based on Topic Weight

et al. 2019

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“…The identification of terms, topics or concepts that are important within a community gives interesting information about its current opinion trends. -Social networks are the specific kind of communities addressed by the paper of Forestier et al [2]. The analysis of community member interactions provides some insight into the organization and the roles existing in a social network in one specific case [2].…”

Section: Papers Of the Special Issuementioning

confidence: 99%

“…-Social networks are the specific kind of communities addressed by the paper of Forestier et al [2]. The analysis of community member interactions provides some insight into the organization and the roles existing in a social network in one specific case [2]. -Tchuente et al [6] also consider that social networks are a specific kind of community.…”

Section: Papers Of the Special Issuementioning

confidence: 99%

Introducing Web Intelligence for communities

Vercouter

Maret

2012

Web Intelligence and Agent Systems: An International Journal

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“…In order to analyze this social network, the current commonly used method is to extract important features in networks. This means converting a larger network to a smaller network, which is an effective summary of the larger network, and remains the important features of the original network [2]. In social networks, this approach can be achieved through two ways, one is to identify users' groups, that is the community division or discovery.…”

Section: Introductionmentioning

confidence: 99%

Entropy-based Social Network Link Partition Algorithm

Zhang¹,

Liang²,

Zhou³

et al. 2018

Proceedings of the 2018 2nd International Conference on Advances in Energy, Environment and Chemical Science (AEECS 2018)

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It is an important core issue in social networks to divide community or group. And, the network node is the mainstream of community division algorithm as the processing object to divide the network. This paper introduces the entropy theory into social networks partition on the basic of studying the concept of entropy and social networks partition algorithm. And we proposed an Entropy-based Link Partition algorithm (ELP algorithm), which is the social network links as the processing object. Also, the similarity between two objects is properly defined and improved, which thus is more close to the real situation of the social network. Experimentation on two real-world networks, and we obtained results of community division and compared with other community partition algorithms to verify the effectiveness of the proposed algorithm. The ELP algorithm has a higher accuracy, and communities are more realistic than that generated by either of the Link Clustering algorithm (LC) or the classical Clique Percolation Method (CMP).

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Roles in social networks: Methodologies and research issues

Cited by 30 publications

References 58 publications

High-Value User Identification Based on Topic Weight

High-Value User Identification Based on Topic Weight

Introducing Web Intelligence for communities

Entropy-based Social Network Link Partition Algorithm

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