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Interactive visual graph analytics on the web
 In ICWSM
, 2015
"... We present a webbased network visual analytics platform called GRAPHVIS that combines interactive visualizations with analytic techniques to reveal important patterns and insights for sense making, reasoning, and decisionmaking. The platform is designed with simplicity in mind and allows users ..."
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We present a webbased network visual analytics platform called GRAPHVIS that combines interactive visualizations with analytic techniques to reveal important patterns and insights for sense making, reasoning, and decisionmaking. The platform is designed with simplicity in mind and allows users to visualize and explore networks in seconds with a simple draganddrop of a graph file into the web browser. GRAPHVIS is fast and flexible, webbased, requires no installation, while supporting a wide range of graph formats as well as stateoftheart visualization and analytic techniques. In particular, the multilevel network analysis engine of GRAPHVIS gives rise to a variety of new possibilities for exploring, analyzing, and understanding complex networks interactively in realtime. Finally, we also highlight other key aspects including filtering, querying, ranking, manipulating, exporting, partitioning (community/role discovery), as well as tools for dynamic network analysis and visualization, interactive graph generators (including two new block model approaches), and a variety of multilevel network analysis and statistical techniques.
International Journal of Modern Physics C © World Scientific Publishing Company Social network sampling using spanning trees
, 2015
"... Due to the large scales and limitations in accessing most online social networks, it is hard or infeasible to directly access them in a reasonable amount of time for studying and analysis. Hence, network sampling has emerged as a suitable technique to study and analyze real networks. The main goal o ..."
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Due to the large scales and limitations in accessing most online social networks, it is hard or infeasible to directly access them in a reasonable amount of time for studying and analysis. Hence, network sampling has emerged as a suitable technique to study and analyze real networks. The main goal of sampling online social networks is constructing a small scale sampled network which preserves the most important properties of the original network. In this paper, we propose two sampling algorithms for sampling online social networks using spanning trees. The first proposed sampling algorithm finds several spanning trees from randomly chosen starting nodes; then the edges in these spanning trees are ranked according to the number of times that each edge has appeared in the set of found spanning trees in the given network. The sampled network is then constructed as a subgraph of the original network which contains a fraction of nodes that are incident on highly ranked edges. In order to avoid traversing the entire network, the second sampling algorithm is proposed using partial spanning trees. The second sampling algorithm is similar to the first algorithm except that it uses partial spanning trees. Several experiments are conducted to examine the performance of the proposed sampling algorithms on wellknown real networks. The obtained results in comparison with other popular sampling methods demonstrate the efficiency of
Beyond Triangles: A Distributed Framework for Estimating 3profiles of Large Graphs ∗
"... We study the problem of approximating the 3profile of a large graph. 3profiles are generalizations of triangle counts that specify the number of times a small graph appears as an induced subgraph of a large graph. Our algorithm uses the novel concept of 3profile sparsifiers: sparse graphs that ca ..."
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We study the problem of approximating the 3profile of a large graph. 3profiles are generalizations of triangle counts that specify the number of times a small graph appears as an induced subgraph of a large graph. Our algorithm uses the novel concept of 3profile sparsifiers: sparse graphs that can be used to approximate the full 3profile counts for a given large graph. Further, we study the problem of estimating local and ego 3profiles, two graph quantities that characterize the local neighborhood of each vertex of a graph. Our algorithm is distributed and operates as a vertex program over the GraphLab PowerGraph framework. We introduce the concept of edge pivoting which allows us to collect 2hop information without maintaining an explicit 2hop neighborhood list at each vertex. This enables the computation of all the local 3profiles in parallel with minimal communication. We test out implementation in several experiments scaling up to 640 cores on Amazon EC2. We find that our algorithm can estimate the 3profile of a graph in approximately the same time as triangle counting. For the harder problem of ego 3profiles, we introduce an algorithm that can estimate profiles of hundreds of thousands of vertices in parallel, in the timescale of minutes.
CHARACTERIZING ACCURACY AND PERFORMANCE TRADEOFFS IN GRAPH SAMPLING FOR GRAPH PROPERTY COMPUTATIONS BY
"... In this thesis, we present a systematic way to characterize the tradeoffs between accuracy and cost in graph sampling. This characterization is heavily dependent on graph structure. Here we focus on vector graph properties, which consist of a value per node in the graph (e.g., PageRank, degree). We ..."
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In this thesis, we present a systematic way to characterize the tradeoffs between accuracy and cost in graph sampling. This characterization is heavily dependent on graph structure. Here we focus on vector graph properties, which consist of a value per node in the graph (e.g., PageRank, degree). We present a new technique for assessing the accuracy of a property based on the algorithm used to compute it. Next, we describe how to interpret several features of accuracyperformance tradeoff curves. Finally, we present our analysis of actual accuracycost curves for both realworld and synthetic graphs. Conclusions from the analysis include that the structure of a graph is more important than its scale for the purposes of sampling, and that different structures require different sampling approaches.
Tracking Triadic Cardinality Distributions for Burst Detection in Social Activity Streams
"... Abstract—In online social networks (OSNs), we often observe abnormally frequent interactions among people before or during some important day, e.g., we receive/send more greetings from/to friends on Christmas Day than usual. We also often observe some viral videos suddenly become worldwide popular t ..."
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Abstract—In online social networks (OSNs), we often observe abnormally frequent interactions among people before or during some important day, e.g., we receive/send more greetings from/to friends on Christmas Day than usual. We also often observe some viral videos suddenly become worldwide popular through one night diffusion in OSNs. Do these seemingly different phenomena share common structure? All these phenomena are related to sudden surges of user activities in OSNs, and are referred to as bursts in this work. We find that the emergence of a burst is accompanied with the formation of new triangles in networks. This finding provokes a new method for detecting bursts in OSNs. We first introduce a new measure, named triadic cardinality distribution, which measures the fraction of nodes with certain number of triangles, i.e., triadic cardinality, in a network. The distribution will change when burst occurs, and is congenitally immunized against spamming social bots. Hence, by tracking triadic cardinality distributions, we are able to detect bursts in OSNs. To relieve the burden of handling huge activity data generated by OSN users, we then develop a delicately designed sampleestimate solution to estimate triadic cardinality distribution efficiently from sampled data. Extensive experiments conducted on real data demonstrate the usefulness of this triadic cardinality distribution and effectiveness of our sampleestimate solution. I.