AD-C: a new node anomaly detection based on community detection in social networks

Keyvanpour, Mohammad Reza; Shirzad, Mehrnoush Barani; Ghaderi, Maryam

doi:10.1504/ijeb.2020.109069

Cited by 16 publications

(2 citation statements)

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“…We outline several points of interest based on the study of each family of community detection papers. 4. We present some possible future trends.…”

Section: Introductionmentioning

confidence: 95%

“…Traditional methods of community detection are based on statistical inference, heuristic approaches, or conventional machine learning [1]. However, despite their past popularity, these strategies are not efficient in the modern era, where the datasets are larger, more complex, and the social relationships over the networks are becoming more complex and much more difficult to define and extract [2][3][4][5][6][7]. In conventional machine learning, community detection has been treated as a problem of forming, extracting, and verifying the accuracy of clusters on large graphs.…”

Section: Introductionmentioning

confidence: 99%

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A Survey on the Recent Advances of Deep Community Detection

2021

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In the first days of social networking, the typical view of a community was a set of user profiles of the same interests and likes, and this community kept enlarging by searching, proposing, and adding new members with the same characteristics that were likely to interfere with the existing members. Today, things have changed dramatically. Social networking platforms are not restricted to forming similar user profiles: The vast amounts of data produced every day have given opportunities to predict and suggest relationships, behaviors, and everyday activities like shopping, food, traveling destinations, etc. Every day, vast data amounts are generated by the famous social networks such as Facebook, Twitter, Instagram, and so on. For example, Facebook alone generates 4 petabytes of data per day. The analysis of such data is of high importance to many aspects like recommendation systems, businesses, health organizations, etc. The community detection problem received considerable attention a long time ago. Communities are represented as clusters of an entire network. Most of the community detection techniques are based on graph structures. In this paper, we present the recent advances of deep learning techniques for community detection. We describe the most recent strategies presented in this field, and we provide some general discussion and some future trends.

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“…We outline several points of interest based on the study of each family of community detection papers. 4. We present some possible future trends.…”

Section: Introductionmentioning

confidence: 95%