A Hierarchical Diffusion Algorithm for Community Detection in Social Networks

Shen, Keyi; Song, Li; Yang, Xiaokang; Zhang, Wenjun

doi:10.1109/cyberc.2010.57

Cited by 16 publications

(23 citation statements)

References 22 publications

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“…After calculating edge weights, nodes are assigned into communities. The clustering follows the same concept of hierarchical diffusion step as in Ref . First, we are add a neighbor node to every leader to be both the core members of their community.…”

Section: The Proposed Approachmentioning

confidence: 99%

A novel social network mining approach for customer segmentation and viral marketing

Helal

Ismail

Badr

et al. 2016

WIREs Data Min & Knowl

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Emergence of social networks facilitates individuals to communicate, share opinions and form communities. Organizations benefit from social networks in monitoring customers’ behavior. Social networks mining and analysis aims to segment customers and determine the most influential actors for viral marketing. In this article, we propose a novel social network mining approach for influential analysis and community detection. The community detection task benefits from the most influential users in the network. The proposed approach identifies the most influential users by using a direct mining leaders discovery algorithm and uses these leaders as core points to expand communities around them. This is based on the observation that communities tend to be formed around users of great influence. Extensive experiments have been completed on a real dataset and results show that our approach can contribute in identifying communities of high quality. WIREs Data Mining Knowl Discov 2016, 6:177–189. doi: 10.1002/widm.1183 This article is categorized under: Fundamental Concepts of Data and Knowledge > Motivation and Emergence of Data Mining Technologies > Structure Discovery and Clustering

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Section: The Proposed Approachmentioning

confidence: 99%

A novel social network mining approach for customer segmentation and viral marketing

Helal

Ismail

Badr

et al. 2016

WIREs Data Min & Knowl

View full text Add to dashboard Cite

show abstract

“…The reduction in the number of variables makes the variable collection task faster and less costly, thus increasing the utility of the tool. For automatic group creation, Fogus et al used the algorithm proposed by [61]. This hierarchical diffusion algorithm is founded on the triadic closure principle, which suggests that, in a social network, there is an increased likelihood that two people will become friends if they have friends in common.…”

Section: Modeling Human Relationships On Social Network Servicesmentioning

confidence: 99%

Open Challenges in Relationship-Based Privacy Mechanisms for Social Network Services

Fogues

Such

Espinosa

et al. 2015

International Journal of Human-Computer Interaction

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Social networking services (SNSs) such as Facebook or Twitter have experienced an explosive growth during the few past years. Millions of users have created their profiles on these services because they experience great benefits in terms of friendship. SNSs can help people to maintain their friendships, organize their social lives, start new friendships, or meet others that share their hobbies and interests. However, all these benefits can be eclipsed by the privacy hazards that affect people in SNSs. People expose intimate information of their lives on SNSs, and this information affects the way others think about them. It is crucial that users be able to control how their information is distributed through the SNSs and decide who can access it. This paper presents a list of privacy threats that can affect SNS users, and what requirements privacy mechanisms should fulfill to prevent this threats. Then, we review current approaches and analyze to what extent they cover the requirements.

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“…Keyi Shen et al [15] proposed a hierarchical 313 diffusion method to detect community structure based on the idea that people in different communities usually share less common friends. Keyi Shen et al [15] proposed a hierarchical 313 diffusion method to detect community structure based on the idea that people in different communities usually share less common friends.…”

Section: Related Workmentioning

confidence: 99%

Detect structural‐connected communities based on BSCHEF in C‐DBLP

Rong

Ying³

et al. 2015

Concurrency and Computation

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Chinese Digital Bibliography & Library Project (C-DBLP) is a huge and real-life co-author social network in China, rarely cited by published paper. It contains a large amount of ground-truth community structure with distinguished research topics. Despite the fact that rich studies on community detection have been conducted with gains of practically fruitful algorithms, unfortunately, with the coming of 'Big Data' era and speedy development of mobile devices, social networks like C-DBLP have incredibly expanded on nodes and edges, as a result, because of massive data cardinality, a large portion of community detection methods consume memory resource excessively. Therefore, in this work, we select Based on Structural Connection Hierarchical Exploration (BSCHE) algorithm to partition nodes in C-DBLP because of its O(n) time cost, fast enough to process massive data, and its novel physical meaning of similarity between nodes defined by structural connection and availability. In addition, in order to avoid huge memory resource consumption caused by 'Big Data' of C-DBLP, we strengthen BSCHE as a framework (BSCHEF) by our proposed 'countpointer-strategy' imitated from incremental batch process to detect co-author communities on C-DBLP. The experiment results show that BSCHEF can find sets of communities on C-DBLP more effectively with the highest modularity value and the least execution time compared to other clustering algorithm.

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A Hierarchical Diffusion Algorithm for Community Detection in Social Networks

Cited by 16 publications

References 22 publications

A novel social network mining approach for customer segmentation and viral marketing

A novel social network mining approach for customer segmentation and viral marketing

Open Challenges in Relationship-Based Privacy Mechanisms for Social Network Services

Detect structural‐connected communities based on BSCHEF in C‐DBLP

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