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Vog: Summarizing and understanding large graphs
, 2014
"... How can we succinctly describe a millionnode graph with a few simple sentences? How can we measure the ‘importance’ of a set of discovered subgraphs in a large graph? These are exactly the problems we focus on. Our main ideas are to construct a ‘vocabulary ’ of subgraphtypes that often occur in re ..."
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How can we succinctly describe a millionnode graph with a few simple sentences? How can we measure the ‘importance’ of a set of discovered subgraphs in a large graph? These are exactly the problems we focus on. Our main ideas are to construct a ‘vocabulary ’ of subgraphtypes that often occur in real graphs (e.g., stars, cliques, chains), and from a set of subgraphs, find the most succinct description of a graph in terms of this vocabulary. We measure success in a wellfounded way by means of the Minimum Description Length (MDL) principle: a subgraph is included in the summary if it decreases the total description length of the graph. Our contributions are threefold: (a) formulation: we provide a principled encoding scheme to choose vocabulary subgraphs; (b) algorithm: we develop VOG, an efficient method to minimize the description cost, and (c) applicability: we report experimental results on multimillionedge real graphs, including Flickr and the Notre Dame web graph. 1
Netray: Visualizing and mining billionscale graphs
 in Adv in Knowledge Discovery and Data Mining
, 2014
"... Abstract. How can we visualize billionscale graphs? How to spot outliers in such graphs quickly? Visualizing graphs is the most direct way of understanding them; however, billionscale graphs are very difficult to visualize since the amount of information overflows the resolution of a typical scre ..."
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Abstract. How can we visualize billionscale graphs? How to spot outliers in such graphs quickly? Visualizing graphs is the most direct way of understanding them; however, billionscale graphs are very difficult to visualize since the amount of information overflows the resolution of a typical screen. In this paper we propose NETRAY, an opensource package for visualizationbased mining on billionscale graphs. NETRAY visualizes graphs using the spy plot (adjacency matrix patterns), distribution plot, and correlation plot which involve careful node ordering and scaling. In addition, NETRAY efficiently summarizes scatter clusters of graphs in a way that finds outliers automatically, and makes it easy to interpret them visually. Extensive experiments show that NETRAY handles very large graphs with billions of nodes and edges efficiently and effectively. Specifically, among the various datasets that we study, we visualize in multiple ways the YahooWeb graph which spans 1.4 billion webpages and 6.6 billion links, and the Twitter whofollowswhom graph, which consists of 62.5 million users and 1.8 billion edges. We report interesting clusters and outliers spotted and summarized by NETRAY. 1
Scalable Graph Exploration and Visualization: Sensemaking Challenges and Opportunities
"... Making sense of large graph datasets is a fundamental and challenging process that advances science, education and technology. We survey research on graph exploration and visualization approaches aimed at addressing this challenge. Different from existing surveys, our investigation highlights appro ..."
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Making sense of large graph datasets is a fundamental and challenging process that advances science, education and technology. We survey research on graph exploration and visualization approaches aimed at addressing this challenge. Different from existing surveys, our investigation highlights approaches that have strong potential in handling large graphs, algorithmically, visually, or interactively; we also explicitly connect relevant works from multiple research fields – data mining, machine learning, humancomputer ineraction, information visualization, information retrieval, and recommender systems – to underline their parallel and complementary contributions to graph sensemaking. We ground our discussion in sensemaking research; we propose a new graph sensemaking hierarchy that categorizes tools and techniques based on how they operate on the graph data (e.g., local vs global). We summarize and compare their strengths and weaknesses, and highlight open challenges. We conclude with future research directions for graph sensemaking.
AdaptiveNav: Discovering Locally Interesting and Surprising Nodes in Large Graphs
"... Visualization is a powerful paradigm for exploratory data analysis. Visualizing large graphs, however, often results in a meaningless hairball. In this paper, we propose a different approach that helps the user adaptively explore large millionnode graphs from a local perspective. For nodes that the ..."
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Visualization is a powerful paradigm for exploratory data analysis. Visualizing large graphs, however, often results in a meaningless hairball. In this paper, we propose a different approach that helps the user adaptively explore large millionnode graphs from a local perspective. For nodes that the user investigates, we propose to only show the neighbors with the most subjectively interesting neighborhoods. We contribute novel ideas to measure this interestingness in terms of how surprising a neighborhood is given the background distribution, as well as how well it fits the nodes the user chose to explore. We are currently designing and developing AdaptiveNav, a fast and scalable method for visually exploring large graphs. By implementing our above ideas, it allows users to look into the forest through its trees.