In this paper, we focus on the problem of discovering internally connected communities in
event-based social networks (EBSNs) and propose a community detection method by …

Social media community detection is a fundamental challenge in social data analytics, in
order to understand user relationships and improve social recommendations. Although the …

Community detection algorithms are important for determining the character statistics of
complex networks. Compared with the conventional community detection algorithms, which …

Community structure is an important characteristic of complex networks. It determines where
important functions of a network are located. Recently, discovering community structure in …

Graph embedding is an effective yet efficient way to convert graph data into a low
dimensional space. In recent years, deep learning has applied on graph embedding and …

Most community detection methods focus on the similarities between detection nodes to
achieve community partitioning. Traditional network representation learning methods are …

The study of community detection in networks has drawn great attention in recent years. To
find communities and to understand community semantics, both network topology and …

We are currently in a network era which enables us to communicate more widely and more
easily via the social networks. Meanwhile, negative information, such as fake news, rumors …

Detecting community structures is an important research topic in social network analysis.
Unfortunately, the fundamental factors that drive the generation of social networks (ie, the …

In social network analysis, community detection is a basic step to understand the structure,
function and semantics of networks. Some conventional community detection methods may …