Description-oriented community detection using exhaustive subgroup discovery

Abstract

a b s t r a c tCommunities can intuitively be defined as subsets of nodes of a graph with a dense structure in the corresponding subgraph. However, for mining such communities usually only structural aspects are taken into account. Typically, no concise nor easily interpretable community description is provided.For tackling this issue, this paper focuses on description-oriented community detection using subgroup discovery. In order to provide both structurally valid and interpretable communities we utilize the graph structure as well as additional descriptive features of the graph's nodes. A descriptive community pattern built upon these features then describes and identifies a community, i.e., a set of nodes, and vice versa. Essentially, we mine patterns in the ''description space'' characterizing interesting sets of nodes (i.e., subgroups) in the ''graph space''; the interestingness of a community is evaluated by a selectable quality measure.We aim at identifying communities according to standard community quality measures, while providing characteristic descriptions of these communities at the same time. For this task, we propose several optimistic estimates of standard community quality functions to be used for efficient pruning of the search space in an exhaustive branch-and-bound algorithm. We demonstrate our approach in an evaluation using five real-world data sets, obtained from three different social media applications.

Exceptional contextual subgraph mining

“…propose to mine the graph topology of a large attributed graph by finding regularities among vertex descriptors. Interestingly, in a recent work Atzmueller et al (2016) use a subgroup discovery approach to mine descriptions of communities, treating the communities as an (aggregated) target.…”

Discovery of Multi-Objective Overlapping Communities within Social Networks using a Socially Inspired Metaheuristic Algorithm

“…They have utilized community discovery algorithms for identifying the set of the most influential nodes in order to begin the spreading process based on an information diffusion model in the networks. Atzmueller et al have presented a description-oriented method for mining structurally valid and interpretable communities utilizing the structure of graph and descriptive features of the nodes of graphs [19].…”

Beyond social graphs: mining patterns underlying social interactions

“…An interesting technique based on a vertices similarity probability (VSP) model for community detection in this situation has been proposed by Li and Pang [56], and a similar approach using a similarity index between nodes can be found in Chen, Xie and Zhang [20], with an application to power grids. Meanwhile, Atzmueller, Doerfel and Mitzlaff [6] do consider the network structure, but also descriptive features of the graph nodes to provide community descriptions.…”