Community detection in social networks is the process of identifying the cohesive groups of
similar nodes. Detection of these groups can be helpful in many applications, such as …

In the first days of social networking, the typical view of a community was a set of user
profiles of the same interests and likes, and this community kept enlarging by searching …

The detection of community structures is a crucial research area. The problem of community
detection has received considerable attention from a large portion of the scientific …

Time-series data are widespread in real-world industrial scenarios. To recover and infer
missing information in real-world applications, the problem of time-series prediction has …

Parallel and distributed community detection in large-scale complex networks, such as
social networks, is a challenging task. Parallel and distributed algorithm with high accuracy …

Bike-sharing systems are becoming popular and generate a large volume of trajectory data.
In a bike-sharing system, users can borrow and return bikes at different stations. In …

Finding semantic communities using network topology and contents together is a hot topic in
community detection. Existing methods often use word attributes in an indiscriminate way to …

Social networks reflect, to a wide extent, the real-world relationships that allow users to
connect and share information. The number of people that interact in social networks keeps …

Due to the rapid development of the sharing economy, shared bikes have become one of
the most popular and convenient traveling tools in intelligent transport systems. Aiming to …

The detection of community structures is a very crucial research area. The problem of
community detection has received considerable attention from a large portion of the …