Abstract—Given a large-scale graph with millions of nodes and 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 struc-tural information of graphs at large-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 algorith-mic 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, large-scale graphs with up to one million nodes and millions of 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. Keywords-graph mining, fast processing of large-scale graphs, graph sense making, large graph visualization I.

Given a large graph with several millions or billions of nodes and edges, such as a social network, how can we explore it eciently and find out what is in the data? In this demo we present Perseus, a large-scale system that enables the comprehensive analysis of large graphs by supporting the coupled summarization of graph properties and structures, guiding attention to outliers, and allowing the user to inter-actively explore normal and anomalous node behaviors. Specifically, Perseus provides for the following opera-tions: 1) It automatically extracts graph invariants (e.g., degree, PageRank, real eigenvectors) by performing scal-able, o✏ine batch processing on Hadoop; 2) It interactively visualizes univariate and bivariate distributions for those in-variants; 3) It summarizes the properties of the nodes that the user selects; 4) It eciently visualizes the induced sub-graph of a selected node and its neighbors, by incrementally revealing its neighbors. In our demonstration, we invite the audience to interact with Perseus to explore a variety of multi-million-edge so-cial networks including a Wikipedia vote network, a friend-ship/foeship network in Slashdot, and a trust network based on the consumer review website Epinions.com. 1.

We defend the thesis that the use of text analytics can boost the results of analyses based on Singular Value Decomposi-tion (SVD). To demonstrate our supposition, rst we model the Digital Bibliography & Library Project (DBLP) as a relational schema; over this schema we use text analytics applied to the terms extracted from the titles of the articles. Then, we apply SVD on the relationships dened between these terms, publication vehicles, and authors; accordingly, we were able to identify the more representative communi-ties and the more active authors relating them to the most meaningful terms and topics found in their respective pub-lications. The results were semantically dense and concise, also leading to performance gains.

Examining spectral space of complex networks with positive and negative links

Examining spectral space of complex networks with positive and negative links