TY - JOUR
T1 - A Temporal User Attribute-based Algorithm to Detect Communities in Online Social Networks
AU - MAHMOUDI, Amin
AU - BAKAR, Azuraliza Abu
AU - SOOKHAK, Mehdi
AU - YAAKUB, Mohd Ridzwan
PY - 2020/8/24
Y1 - 2020/8/24
N2 - 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.
AB - 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.
KW - User attributes
KW - online social network
KW - community detection
KW - gravity model
KW - recently largest interaction
UR - http://www.scopus.com/inward/record.url?scp=85090977457&partnerID=8YFLogxK
U2 - 10.1109/ACCESS.2020.3018941
DO - 10.1109/ACCESS.2020.3018941
M3 - Journal Article (refereed)
SN - 2169-3536
VL - 8
SP - 154363
EP - 154381
JO - IEEE Access
JF - IEEE Access
M1 - 9174735
ER -