03 تیر 1403

امین محمودی (مستعفی)

مرتبه علمی: استادیار
نشانی:
تحصیلات: دکترای تخصصی / علوم کامپیوتر
تلفن:
دانشکده: دانشکده علوم ریاضی و آمار

مشخصات پژوهش

عنوان
A Temporal User Attribute-Based Algorithm to Detect Communities in Online Social Networks
نوع پژوهش مقاله چاپ شده
کلیدواژه‌ها
User attributes, online social network, community detection, gravity model, recently largest interaction
سال
2020
مجله IEEE Access
پژوهشگران امین محمودی (مستعفی)

چکیده

The world is witnessing the daily emergence of a vast variety of online social networks and community detection problem is a major research area in online social network studies. The existing community detection algorithms are mostly edge-based and are evaluated using the modularity metric benchmarks. However, these algorithms have two inherent limitations. Firstly, they are based on a pure mathematical object which considers the number of connections in each community as the main measures. Consequently, a resolution limit and low accuracy in nding community members in often observed. Whereas, online social networks are dynamic networks and the key players are humans whose main attributes such as lifespan, geo-location, the density of interactions, and user weight, change over time. These attributes tend to inuence the formation of user communities in any category of online social network. Secondly, the output structure of existing community detection algorithms is usually provided as a graph and dendrogram. A graph structure, is, however, characterized by a high memory complexity, and subsequently exponential search time complexity. Implementing dendrogram such a complex structure is complicated. To address memory complexity and the accuracy rate of the community detection issues, this paper proposes a new temporal user attribute-based algorithm, namely the recently largest interaction based on the attributes of a typical online social network user. Experimental results show that the proposed algorithm outperforms eight well-known algorithms in this domain.