StructMatrix: billion-scale visualization of planetary graphs by means of structure detection and dense matrices.
Hugo Gualdron, Robson L. F. Cordeiro, Jose Fernando Rodrigues Junior · arXiv (Cornell University) · 2015
Given a planetary-scale graph with millions of nodes and billions of edges, how to reveal macro patterns of interest, like cliques, bi-partite cores, stars, and chains? Furthermore, how to visualize such patterns altogether getting insights from the graph to support wise decision-making? Although there are many algorithmic and visual techniques to analyze graphs, none of the existing approaches is able to present the structural information of graphs at planetary scale. Hence, this paper describes StructMatrix, a methodology aimed at high scalable visual inspection of graph structures with the goal of revealing macro patterns of interest. StructMatrix combines algorithmic structure detection and adjacency matrix visualization to present cardinality, distribution, and relationship features of the structures found in a given graph. We performed experiments in real, planetary-scale graphs with up to millions of nodes and over 10 billion edges. StructMatrix revealed that graphs of high relevance (e.g., Web, Wikipedia and DBLP) have characterizations that reflect the nature of their corresponding domains; our findings have not been seen in the literature so far. We expect that our technique will bring deeper insights into large graph mining, leveraging their use for decision making.