Subgroup discovery and community detection are standard approaches for identifying (cohesive) subgroups. This paper presents an organized picture of recent research in descriptive community (and subgroup) detection. Here, it summarizes approaches for the identification of descriptive patterns targeting both static as well as dynamic (sequential) relations. We specifically focus on attributed gr...

Community detection refers to the problem of clustering nodes a network (either graph or hypergrah) into groups. Various algorithms are available for community and all these methods apply uncensored networks. In practice, may has censored (or missing) values it is shown that have non-negligible effect on structural properties network. this paper, we study in $m$-uniform hypergraph from informat...

Many networks of scientific interest naturally decompose into clusters or communities with comparatively fewer external than internal links; however, current Bayesian models of network communities do not exert this intuitive notion of communities. We formulate a nonparametric Bayesian model for community detection consistent with an intuitive definition of communities and present a Markov chain...

Complex networks can usually be divided in dense subnetworks called communities. In evolving networks, the usual way to detect communities is to find several partitions independently, one for each time step. However, this generally causes troubles when trying to track communities from one time step to the next. We propose here a new method to detect only one decomposition in communities that is...