… This paper focuses on detecting communities in social networks from a new perspective -
emotional behaviors. Emotional community means that users in same community are more …

… of social network analysis related to community detection and uses … social media data. It
is a self-contained book and covers important topics regarding community detection in social …

… the social network community detection and … the community detection algorithm, which uses
module degree with interest degree and personal willingness to make community detection; …

… Statistical Inference based methods (SBM) and Bayesian Inference Used to model and
analyze real graphs such as social network. DBOCD overcomes drawbacks of BNMTF, it can …

… Given a directed social graph and a set of past information … problem of detecting modules
of the graph (communities of … propagation and social ties formation in a social network can …

… Let us imagine that we have a collegiate social network that we want to use to evaluate the
quality of a community detection algorithm. Assume that in addition to the social network, we …

Community detection algorithms are fundamental tools that allow us to uncover … When
detecting communities, there are two possible sources of information one can use: the network …

… of disciplines aiming to fully obtain a network’s community information. The interested reader
is … on the existing literature for community detection in social networks which motivated us to …

… Recently, researchers have performed a longitudinal study on the community detection
task in which the social network is monitored at regular time intervals over a period of time.Time …

… community detection approach which combines both social objects clustering and link
analysis. We first use a subspace clustering algorithm to group all the social … the social network …