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arXiv · 1306.1300

Personalized Email Community Detection using Collaborative Similarity Measure

Waqas Nawaz·Yongkoo Han·Kifayat-Ullah Khan·Young-Koo Lee
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Abstract

Email service providers have employed many email classification and prioritization systems over the last decade to improve their services. In order to assist email services, we propose a personalized email community detection method to discover the groupings of email users based on their structural and semantic intimacy. We extract the personalized social graph from a set of emails by uniquely leveraging each node with communication behavior. Subsequently, collaborative similarity measure (CSM) based intra-graph clustering approach detects personalized communities. The empirical analysis shows effectiveness of the resultant communities in terms of evaluation measures, i.e. density, entropy and f-measure. Moreover, email strainer, dynamic group prediction, and fraudulent account detection are suggested as the potential applications from both the service provider and user's point of view.

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Waqas Nawaz, Yongkoo Han, Kifayat-Ullah Khan, Young-Koo Lee. 2013-06-06. Personalized Email Community Detection using Collaborative Similarity Measure. https://arxiv.org/abs/1306.1300

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