Abstract
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 finding 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 influence 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.
Highlights
Existing studies mostly overlooked the role of user attributes
These attributes are similar to the universal gravity formula, which provided a substratum for the development of our novel approach: the recently largest interaction (RLI) algorithm, which consists of five sub algorithms
The results showed that the proposed RLI algorithm can detect communities better than the eight standard existing algorithms and that it can reduce memory complexity significantly
Summary
The huge amount of transactions on OSNs provide a good opportunity to extract relations between two or more people, which has been termed the community detection (CD) problem. CD in networks is one of the most important problems currently being considered by numerous researchers working in the computer science field because it can be used for multitude of purposes, including recommender systems, cybersecurity, communication studies, and information science. It was designed to work on static networks (wherein the nature of the nodes and edges will not change over time) and was tested on a physics collaboration network. As the GN algorithm considers time complexity and can function in a large-scale network, many researchers became interested in this domain [5][6][7][8][9][10][11][12]
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