Disassortative Degree Mixing and Information Diffusion for Overlapping Community Detection in Social Networks (DMID)

Shahriari, Mohsen; Krott, Sebastian; Klamma, Ralf

doi:10.1145/2740908.2741696

Cited by 7 publications

(3 citation statements)

References 32 publications

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“…Instead of considering the entire user-user network as a single community, this method regards the network as the combination of multiple user communities. Thereby, it detects overlapping communities [54] and differentiate the impact of intracommunity and inter-community users to other users in the user-user network.…”

Section: Hits and Its Variantsmentioning

confidence: 99%

A Survey on Expert Recommendation in Community Question Answering

Wang

Huang

Yao

et al. 2018

J. Comput. Sci. Technol.

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Community question answering (CQA) represents the type of Web applications where people can exchange knowledge via asking and answering questions. One significant challenge of most real-world CQA systems is the lack of effective matching between questions and the potential good answerers, which adversely affects the efficient knowledge acquisition and circulation. On the one hand, a requester might experience many low-quality answers without receiving a quality response in a brief time; on the other hand, an answerer might face numerous new questions without being able to identify their questions of interest quickly. Under this situation, expert recommendation emerges as a promising technique to address the above issues. Instead of passively waiting for users to browse and find their questions of interest, an expert recommendation method raises the attention of users to the appropriate questions actively and promptly. The past few years have witnessed considerable efforts that address the expert recommendation problem from different perspectives. These methods all have their issues that need to be resolved before the advantages of expert recommendation can be fully embraced. In this survey, we first present an overview of the research efforts and state-of-the-art techniques for the expert recommendation in CQA. We next summarize and compare the existing methods concerning their advantages and shortcomings, followed by discussing the open issues and future research directions.

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Section: Hits and Its Variantsmentioning

confidence: 99%

A Survey on Expert Recommendation in Community Question Answering

Wang

Huang

Yao

et al. 2018

J. Comput. Sci. Technol.

View full text Add to dashboard Cite

show abstract

“…One of the initial works was the optimization proposed by Newman et al [16]. It was followed by other local approaches using random walk processes [25], information diffusion [16,28] and leader-based [23] approaches. Although there were many approaches for OCD, few of them use and find a real application scenario for the usability of such algorithms [17,15].…”

Section: Ocd and Ranking Algorithms For Expert Identification In Qafsmentioning

confidence: 99%

“…DMID is a two-phase OCD algorithm majorly suitable for disassortative degree mixing networks [23]. In the first phase, it combines disassortative degree mixing property along with the degree of nodes to identify leaders.…”

Section: Disassortative Degree Mixing and Information Diffusion (Dmid)mentioning

confidence: 99%

Community-aware ranking algorithms for expert identification in question-answer forums

Shahriari

Parekodi

Klamma

2015

Proceedings of the 15th International Conference on Knowledge Technologies and Data-Driven Business

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Question-Answer forums (QAF) are significant platforms for disseminating informal information and play important role in problem solving and learning. Expert identification still has some limitations and link analysis methods do not consider community dimension. In this paper an authority analysis approach for identifying experts is proposed. This approach combines overlapping community detection (OCD) algorithms with ranking methods to compute the nodes' expertise level in QAFs. Firstly, graph resulting from a specific search query is computed and an OCD algorithm is applied on it. After identifying clusters of nodes, we change updating rules of original Hyperlink-Induced Topic Search (HITS) and PageRank to take the effect of intra cluster links and extra cluster connections. People whom are intra or overlapping to a community possess higher vision about context of the community than nodes which are outside. We experimented the proposed overlapping community-aware ranking algorithms and compared them with baseline approaches on online forums. Results indicate that OCD improves expert identification accuracy and relevancy.

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The Significant Effect of Overlapping Community Structures in Signed Social Networks

Shahriari

Liu

Klamma

2017

Lecture Notes in Social Networks

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Many real-world networks can be modeled by networks of interacting agents. Analysis of these interactions can reveal fundamental properties from these networks. Estimating the amount of collaboration in a network corresponding to connections in a learning environment can reveal to what extent learners share their experience and knowledge with other learners. Alternatively, analyzing the network of interactions in an open source software project can manifest indicators showing the efficiency of collaborations. One central problem in such domains is the low cooperativity values of networks due to the low cooperativity values of their respective communities. So administrators should not only understand and predict the cooperativity of networks but also they need to evaluate their respective community structures. To approach this issue, in this paper, we address two domains of open source software projects and learning forums. As such, we calculate the amount of cooperativity in the corresponding networks and communities of these domains by applying several community detection algorithms. Moreover, we investigated the community properties and identified the significant properties for estimating the network and community cooperativity. Correspondingly, we identified to what extent various community detection algorithms affect the identification of significant properties and prediction of cooperativity. We also fabricated binary and regression prediction models using the community properties. Our results and constructed models can be used to infer cooperativity of community structures from their respective properties. When predicting high defective structures in networks, administrators can look for useful drives to increase the collaborations.

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Disassortative Degree Mixing and Information Diffusion for Overlapping Community Detection in Social Networks (DMID)

Cited by 7 publications

References 32 publications

A Survey on Expert Recommendation in Community Question Answering

A Survey on Expert Recommendation in Community Question Answering

Community-aware ranking algorithms for expert identification in question-answer forums

The Significant Effect of Overlapping Community Structures in Signed Social Networks

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