A new method for graph stream summarization based on both the structure and concepts

Abstract

Graph datasets are common in many application domains and for which their graphs are usually massive. One solution to process such massive graphs is summarization. There are two kinds of graphs, stationary and stream. For stationary graphs, a number of summarization algorithms are available while for graph stream there is no a comprehensive summarization method that summarizes a graph stream based on the structure, vertex attributes or both with varying contributions. This is because of challenges of graph stream, which are volume of data and changing of data over time. In this paper, we propose a method based on sliding-window model for which summarizes a graph stream based on a combination of the structure and vertex attributes. We proposed a new structure for summary graphs and also proposed new methods for comparing two summary graphs. To the best of our knowledge, this is the first method that summarizes a graph stream based on both the structure and vertex attributes with varying contributions. Through extensive experiments on real dataset of Amazon co-purchasing products, we have demonstrated the performance of the proposed method.

A Markov Chain-Based Overlapping Community Detection Algorithm for Complex Networks

“…Both the GN and the fast algorithm divides the hierarchical tree with the objective function of maximum modularity. Ashrafi Payaman and Kangavari [9] mapped the geodesic distance of network node pair to high-dimensional space and performed sparse linear coding to achieve spectral clustering. Jarukasemratana et al [10] judged the number of communities by node distance and density.…”