Clustering attributed graphs: Models, measures and methods

Clustering attributed graphs: Models, measures and methods

Clustering a graph, i.e., assigning its nodes to groups, is an important operation whose best known application is the discovery of communities in social networks. Graph clustering and community detection have traditionally focused on graphs without attributes, with the notable exception of edge weights. However, these models only provide a partial representation of real social systems, that ar...

Distance Measures between Attributed Graphs and Second-Order Random Graphs

The aim of this article is to purpose a distance measure between Attributed Graphs (AGs) and Second-Order Random Graphs (SORGs) for recognition and classification proposes. The basic feature of SORGs is that they include both marginal probability functions and joint probability functions of graph elements (vertices or arcs). This allows a more precise description of both the structural and sema...

Clustering Attributed Multi-graphs with Information Ranking

Attributed multi-graphs are data structures to model realworld networks of objects which have rich properties/attributes and they are connected by multiple types of edges. Clustering attributed multigraphs has several real-world applications, such as recommendation systems and targeted advertisement. In this paper, we propose an efficient method for Clustering Attributed Multi-graphs with Infor...

Probabilistic prototype models for attributed graphs

This contribution proposes a new approach towards developing a class of probabilistic methods for classifying attributed graphs. The key concept is random attributed graph, which is defined as an attributed graph whose nodes and edges are annotated by random variables. Every node/edge has two random processes associated with itoccurence probability and the probability distribution over the attr...

Parameter-free Outlier-aware Clustering on Attributed Graphs