%0 Conference Paper %1 Akoglu:2012:OMV:2213836.2213941 %A Akoglu, Leman %A Chau, Duen Horng %A Kang, U. %A Koutra, Danai %A Faloutsos, Christos %B Proceedings of the 2012 ACM SIGMOD International Conference on Management of Data %C New York, NY, USA %D 2012 %I ACM %K analytics graph large mining visualisation %P 717--720 %R 10.1145/2213836.2213941 %T OPAvion: mining and visualization in large graphs %U http://doi.acm.org/10.1145/2213836.2213941 %X Given a large graph with millions or billions of nodes and edges, like a who-follows-whom Twitter graph, how do we scalably compute its statistics, summarize its patterns, spot anomalies, visualize and make sense of it? We present OPAvion, a graph mining system that provides a scalable, interactive workflow to accomplish these analysis tasks. OPAvion consists of three modules: (1) The Summarization module (Pegasus) operates off-line on massive, disk-resident graphs and computes graph statistics, like PageRank scores, connected components, degree distribution, triangles, etc.; (2) The Anomaly Detection module (OddBall) uses graph statistics to mine patterns and spot anomalies, such as nodes with many contacts but few interactions with them (possibly telemarketers); (3) The Interactive Visualization module (Apolo) lets users incrementally explore the graph, starting with their chosen nodes or the flagged anomalous nodes; then users can expand to the nodes' vicinities, label them into categories, and thus interactively navigate the interesting parts of the graph. In our demonstration, we invite our audience to interact with OPAvion and try out its core capabilities on the Stack Overflow Q&A graph that describes over 6 million questions and answers among 650K users. %@ 978-1-4503-1247-9

@inproceedings{Akoglu:2012:OMV:2213836.2213941, abstract = {Given a large graph with millions or billions of nodes and edges, like a who-follows-whom Twitter graph, how do we scalably compute its statistics, summarize its patterns, spot anomalies, visualize and make sense of it? We present OPAvion, a graph mining system that provides a scalable, interactive workflow to accomplish these analysis tasks. OPAvion consists of three modules: (1) The Summarization module (Pegasus) operates off-line on massive, disk-resident graphs and computes graph statistics, like PageRank scores, connected components, degree distribution, triangles, etc.; (2) The Anomaly Detection module (OddBall) uses graph statistics to mine patterns and spot anomalies, such as nodes with many contacts but few interactions with them (possibly telemarketers); (3) The Interactive Visualization module (Apolo) lets users incrementally explore the graph, starting with their chosen nodes or the flagged anomalous nodes; then users can expand to the nodes' vicinities, label them into categories, and thus interactively navigate the interesting parts of the graph. In our demonstration, we invite our audience to interact with OPAvion and try out its core capabilities on the Stack Overflow Q&A graph that describes over 6 million questions and answers among 650K users.}, acmid = {2213941}, added-at = {2013-08-01T13:24:54.000+0200}, address = {New York, NY, USA}, author = {Akoglu, Leman and Chau, Duen Horng and Kang, U. and Koutra, Danai and Faloutsos, Christos}, biburl = {https://www.bibsonomy.org/bibtex/241f8640f4268fe1e81feac522b005365/rwoz}, booktitle = {Proceedings of the 2012 ACM SIGMOD International Conference on Management of Data}, description = {OPAvion}, doi = {10.1145/2213836.2213941}, interhash = {bd0720711f1f54fd4456cbd07b37dafe}, intrahash = {41f8640f4268fe1e81feac522b005365}, isbn = {978-1-4503-1247-9}, keywords = {analytics graph large mining visualisation}, location = {Scottsdale, Arizona, USA}, numpages = {4}, pages = {717--720}, publisher = {ACM}, series = {SIGMOD '12}, timestamp = {2013-08-01T13:28:31.000+0200}, title = {OPAvion: mining and visualization in large graphs}, url = {http://doi.acm.org/10.1145/2213836.2213941}, year = 2012 }