{"type":"ScholarlyArticle","title":"Query-Driven Discovery of Anomalous Subgraphs in Attributed Graphs","authors":[{"name":"Nannan Wu","slug":"nannan-wu","given":"Nannan","family":"Wu","affiliation":"* Dept. of Computer Science & Engineering , Beihang University , Beijing 100191 , China"},{"name":"Feng Chen","slug":"feng-chen","given":"Feng","family":"Chen","affiliation":"† Dept. of Computer Science , University at Albany , SUNY , Albany , NY 12203"},{"name":"Jianxin Li","slug":"jianxin-li","given":"Jianxin","family":"Li","affiliation":"* Dept. of Computer Science & Engineering , Beihang University , Beijing 100191 , China"},{"name":"Jinpeng Huai","slug":"jinpeng-huai","given":"Jinpeng","family":"Huai","affiliation":"* Dept. of Computer Science & Engineering , Beihang University , Beijing 100191 , China"},{"name":"Bo Li","slug":"bo-li","given":"Bo","family":"Li","affiliation":"* Dept. of Computer Science & Engineering , Beihang University , Beijing 100191 , China"}],"abstract":"For a detection problem, a user often has some prior knowledge about the structure-specific subgraphs of interest, but few traditional approaches are capable of employing this knowledge. The main technical challenge is that few approaches can efficiently model the space of connected subgraphs that are isomorphic to a query graph. We present a novel, efficient approach for optimizing a generic nonlinear cost function subject to a query-specific structural constraint. Our approach enjoys strong theoretical guarantees on the convergence of a nearly optimal solution and a low time complexity. For the case study, we specialize the nonlinear function to several well-known graph scan statistics for anomalous subgraph discovery. Empirical evidence demonstrates that our method is superior to state-of-the-art methods in several real-world anomaly detection tasks.","doi":"10.24963/ijcai.2017/433","url":"https://ijcai.org/proceedings/2017/433","pdf":"https://ijcai.org/proceedings/2017/0433.pdf","markdown":"https://ijcai.org/proceedings/2017/433.md","bibtex":"https://ijcai.org/proceedings/2017/bibtex/433","volume":{"slug":"2017","title":"Proceedings of the Twenty-Sixth International Joint Conference on Artificial Intelligence","short_title":"IJCAI 2017","url":"https://ijcai.org/proceedings/2017/","year":2017,"location":"Melbourne, Australia","dates":"19-25 August 2017","editors":"Carles Sierra","isbn":"978-0-9992411-0-3","publisher":"International Joint Conferences on Artificial Intelligence Organization","doi_prefix":"10.24963/ijcai.2017"},"section":"Machine Learning S-Z","track":"Main track","number":433,"pages":{"first":3105,"last":3111},"keywords":["Machine Learning: Structured Learning","Combinatorial & Heuristic Search: Modeling","Machine Learning: Learning Theory","Constraints and Satisfiability: Constraint Optimisation"],"videos":[],"references":[{"position":1,"raw":"[Bach et al., 2011] Francis Bach, Rodolphe Jenatton, Julien Mairal, and Guillaume Obozinski. Structured spar- sity through convex optimization. Statistical Science, 27(4):450-468, 2011.","title":"Structured sparsity through convex optimization","authors":"Francis Bach, Rodolphe Jenatton, Julien Mairal, Guillaume Obozinski","year":"2011","venue":"Statistical Science","doi":null,"url":null,"internal_url":null},{"position":2,"raw":"[Bahmani et al., 2013] Sohail Bahmani, Bhiksha Raj, and Petros T. Boufounos. Greedy sparsity-constrained opti- mization. JMLR, 14(1):807-841, 2013.","title":"Greedy sparsity-constrained optimization","authors":"Sohail Bahmani, Bhiksha Raj, Petros T Boufounos","year":"2013","venue":"JMLR","doi":null,"url":null,"internal_url":null},{"position":3,"raw":"[Bahmani et al., 2016] Sohail Bahmani, Petros T. Boufounos, and Bhiksha Raj. Learning model-based sparsity via projected gradient descent. IEEE Trans. Inf. Theory, 62(4):2092-2099, 2016.","title":"Learning model-based sparsity via projected gradient descent","authors":"Sohail Bahmani, Petros T Boufounos, Bhiksha Raj","year":"2016","venue":"IEEE Trans. Inf. Theory","doi":null,"url":null,"internal_url":null},{"position":4,"raw":"[Chen and Neill, 2014] Feng Chen and Daniel B. Neill. Non- parametric scan statistics for event detection and forecast- ing in heterogeneous social media graphs. In The 20th ACM SIGKDD International Conference on Knowledge Discovery and Data Mining, KDD '14, New York, NY, USA -August 24 -27, 2014, pages 1166-1175, 2014.","title":"Nonparametric scan statistics for event detection and forecasting in heterogeneous social media graphs","authors":"Chen, Neill ; ] Feng Chen, Daniel B Neill","year":"2014","venue":"The 20th ACM SIGKDD International Conference on Knowledge Discovery and Data Mining, KDD '14","doi":null,"url":null,"internal_url":null},{"position":5,"raw":"[Chen and Zhou, 2016] Feng Chen and Baojian Zhou. A generalized matching pursuit approach for graph- structured sparsity. In Proceedings of the Twenty-Fifth International Joint Conference on Artificial Intelligence, IJCAI'16, 9-15 July 2016, pages 1389-1395, 2016.","title":"A generalized matching pursuit approach for graphstructured sparsity","authors":"Chen, Zhou ; ] Feng Chen, Baojian Zhou","year":"2016","venue":"Proceedings of the Twenty-Fifth International Joint Conference on Artificial Intelligence, IJCAI'16","doi":null,"url":null,"internal_url":null},{"position":6,"raw":"[Choi et al., 2009] Hyunsang Choi, Heejo Lee, and Hyogon Kim. Botgad: detecting botnets by capturing group activ- ities in network traffic. In Proceedings of the Fourth In- ternational ICST Conference on COMmunication System softWAre and middlewaRE, page 2. ACM, 2009.","title":"Botgad: detecting botnets by capturing group activities in network traffic","authors":"Choi","year":"2009","venue":"Proceedings of the Fourth International ICST Conference on COMmunication System softWAre and middlewaRE","doi":null,"url":null,"internal_url":null},{"position":7,"raw":"[Du and Yang, 2011] Haitao Du and Shanchieh Jay Yang. Discovering collaborative cyber attack patterns using so- cial network analysis. In International Conference on So- cial Computing, Behavioral-Cultural Modeling, and Pre- diction, pages 129-136. Springer, 2011.","title":"Discovering collaborative cyber attack patterns using social network analysis","authors":"Haitao Du, Shanchieh Jay, Yang","year":"2011","venue":"International Conference on Social Computing, Behavioral-Cultural Modeling, and Prediction","doi":null,"url":null,"internal_url":null},{"position":8,"raw":"[Gionis et al., 2015] Aristides Gionis, Michael Math- ioudakis, and Antti Ukkonen. Bump hunting in the dark: Local discrepancy maximization on graphs. In 31st IEEE International Conference on Data Engineering, ICDE 2015, April 13-17, 2015, pages 1155-1166, 2015.","title":"Bump hunting in the dark: Local discrepancy maximization on graphs","authors":"Gionis","year":"2015","venue":"31st IEEE International Conference on Data Engineering, ICDE 2015","doi":null,"url":null,"internal_url":null},{"position":9,"raw":"[Gupta et al., 2014] Manish Gupta, Jing Gao, Xifeng Yan, Hasan Cam, and Jiawei Han. Top-k interesting subgraph discovery in information networks. In 2014 IEEE 30th In- ternational Conference on Data Engineering, pages 820- 831. IEEE, 2014.","title":"Top-k interesting subgraph discovery in information networks","authors":"Manish Gupta, Jing Gao, Xifeng Yan, Cam Hasan, Jiawei Han","year":"2014","venue":"IEEE 30th International Conference on Data Engineering","doi":null,"url":null,"internal_url":null},{"position":10,"raw":"[Hegde et al., 2014] Chinmay Hegde, Piotr Indyk, and Lud- wig Schmidt. Approximation-tolerant model-based com- pressive sensing. In Proceedings of the Twenty-Fifth Annual ACM-SIAM Symposium on Discrete Algorithms, SODA 2014, January 5-7, 2014, pages 1544-1561, 2014.","title":"Approximation-tolerant model-based compressive sensing","authors":"Chinmay Hegde, Piotr Indyk, Ludwig Schmidt","year":"2014","venue":"Proceedings of the Twenty-Fifth Annual ACM-SIAM Symposium on Discrete Algorithms, SODA 2014","doi":null,"url":null,"internal_url":null},{"position":11,"raw":"[Hegde et al., 2015] Chinmay Hegde, Piotr Indyk, and Lud- wig Schmidt. A nearly-linear time framework for graph- structured sparsity. In Proceedings of the 32nd Inter- national Conference on Machine Learning, ICML 2015, Lille, France, 6-11 July 2015, pages 928-937, 2015.","title":"A nearly-linear time framework for graphstructured sparsity","authors":"Chinmay Hegde, Piotr Indyk, Ludwig Schmidt","year":"2015","venue":"Proceedings of the 32nd International Conference on Machine Learning, ICML 2015","doi":null,"url":null,"internal_url":null},{"position":12,"raw":"[Jain et al., 2014] Prateek Jain, Ambuj Tewari, and Pu- rushottam Kar. On iterative hard thresholding methods for high-dimensional m-estimation. In Advances in Neural In- formation Processing Systems 27: Annual Conference on Neural Information Processing Systems 2014, Dec. 8-13 2014, Montreal, Quebec, Canada, pages 685-693, 2014.","title":"On iterative hard thresholding methods for high-dimensional m-estimation","authors":"Prateek Jain, Ambuj Tewari, Purushottam Kar","year":"2014","venue":"Advances in Neural Information Processing Systems 27: Annual Conference on Neural Information Processing Systems 2014","doi":null,"url":null,"internal_url":null},{"position":13,"raw":"[Kim et al., 2015] Jinha Kim, Hyungyu Shin, Wook-Shin Han, Sungpack Hong, and Hassan Chafi. Taming subgraph isomorphism for rdf query processing. Proceedings of the VLDB Endowment, 8(11):1238-1249, 2015.","title":"Taming subgraph isomorphism for rdf query processing","authors":"Jinha Kim, Hyungyu Shin, Wook-Shin Han, Sungpack Hong, Hassan Chafi","year":"2015","venue":"Proceedings of the VLDB Endowment","doi":null,"url":null,"internal_url":null},{"position":14,"raw":"[McFowland et al., 2013] Edward McFowland, Skyler Speakman, and Daniel B Neill. Fast generalized subset scan for anomalous pattern detection. Journal of Machine Learning Research, 14(1):1533-1561, 2013.","title":"Fast generalized subset scan for anomalous pattern detection","authors":"Edward Mcfowland, Skyler Speakman, Daniel B Neill","year":"2013","venue":"Journal of Machine Learning Research","doi":null,"url":null,"internal_url":null},{"position":15,"raw":"[Neill, 2009] Daniel B Neill. An empirical comparison of spatial scan statistics for outbreak detection. International journal of health geographics, 8(1):1, 2009.","title":"An empirical comparison of spatial scan statistics for outbreak detection","authors":"B Daniel, Neill","year":"2009","venue":"International journal of health geographics","doi":null,"url":null,"internal_url":null},{"position":16,"raw":"[Pan et al., 2013] Bei Pan, Yu Zheng, David Wilkie, and Cyrus Shahabi. Crowd sensing of traffic anomalies based on human mobility and social media. 2013.","title":"Crowd sensing of traffic anomalies based on human mobility and social media","authors":"Pan","year":"2013","venue":null,"doi":null,"url":null,"internal_url":null},{"position":17,"raw":"[Patil et al., 2003] GP Patil, C Taillie, et al. Geographic and network surveillance via scan statistics for critical area de- tection. Statistical Science, 18(4):457-465, 2003.","title":"Geographic and network surveillance via scan statistics for critical area detection","authors":"Patil","year":"2003","venue":"Statistical Science","doi":null,"url":null,"internal_url":null},{"position":18,"raw":"[Qian et al., 2014] Jing Qian, Venkatesh Saligrama, and Yut- ing Chen. Connected sub-graph detection. In AISTATS '14, Iceland, April 22-25, 2014, pages 796-804, 2014.","title":"Connected sub-graph detection","authors":"Jing Qian, Venkatesh Saligrama, Yuting Chen","year":"2014","venue":"AISTATS '14","doi":null,"url":null,"internal_url":null},{"position":19,"raw":"[Tong et al., 2007] Hanghang Tong, Christos Faloutsos, Brian Gallagher, and Tina Eliassi-Rad. Fast best-effort pattern matching in large attributed graphs. In Proceed- ings of the 13th ACM SIGKDD International Conference on Knowledge Discovery and Data Mining, San Jose, CA, USA, August 12-15, 2007, pages 737-746, 2007.","title":"Fast best-effort pattern matching in large attributed graphs","authors":"Tong","year":"2007","venue":"Proceedings of the 13th ACM SIGKDD International Conference on Knowledge Discovery and Data Mining","doi":null,"url":null,"internal_url":null},{"position":20,"raw":"[Wu et al., 2016] Nannan Wu, Feng Chen, Jianxin Li, Bao- jian Zhou, and Naren Ramakrishnan. Efficient nonpara- metric subgraph detection using tree shaped priors. In Pro- ceedings of the Thirtieth AAAI Conference on Artificial In- telligence, February 12-17, 2016, Phoenix, Arizona, USA., pages 1352-1358, 2016.","title":"Efficient nonparametric subgraph detection using tree shaped priors","authors":"Nannan Wu, Feng Chen, Jianxin Li, Baojian Zhou, Naren Ramakrishnan","year":"2016","venue":"Proceedings of the Thirtieth AAAI Conference on Artificial Intelligence","doi":null,"url":null,"internal_url":null},{"position":21,"raw":"[Yang et al., 2014] Shengqi Yang, Yinghui Wu, Huan Sun, and Xifeng Yan. Schemaless and structureless graph querying. Proceedings of the VLDB Endowment, 7(7):565-576, 2014.","title":"Schemaless and structureless graph querying","authors":"Yang","year":"2014","venue":"Proceedings of the VLDB Endowment","doi":null,"url":null,"internal_url":null},{"position":22,"raw":"[Yang et al., 2016] Shengqi Yang, Fangqiu Han, Yinghui Wu, and Xifeng Yan. Fast top-k search in knowledge graphs. In 32nd IEEE International Conference on Data Engineering, ICDE 2016, Helsinki, Finland, May 16-20, 2016, pages 990-1001, 2016.","title":"Fast top-k search in knowledge graphs","authors":"Yang","year":"2016","venue":"32nd IEEE International Conference on Data Engineering, ICDE 2016","doi":null,"url":null,"internal_url":null},{"position":23,"raw":"[Yuan and Liu, 2014] Xiao-Tong Yuan and Qingshan Liu. Newton greedy pursuit: A quadratic approximation method for sparsity-constrained optimization. In Proceed- ings of the IEEE Conference on Computer Vision and Pat- tern Recognition, pages 4122-4129, 2014.","title":"Newton greedy pursuit: A quadratic approximation method for sparsity-constrained optimization","authors":"Xiao-Tong Yuan, Qingshan Liu","year":"2014","venue":"Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition","doi":null,"url":null,"internal_url":null},{"position":24,"raw":"[Yuan et al., 2014] Xiaotong Yuan, Ping Li, and Tong Zhang. Gradient hard thresholding pursuit for sparsity- constrained optimization. In Proceedings of the 31th In- ternational Conference on Machine Learning, ICML 2014, Beijing, China, 21-26 June 2014, pages 127-135, 2014.","title":"Gradient hard thresholding pursuit for sparsityconstrained optimization","authors":"Xiaotong Yuan, Ping Li, Tong Zhang","year":"2014","venue":"Proceedings of the 31th International Conference on Machine Learning, ICML 2014","doi":null,"url":null,"internal_url":null}],"license_note":"Copyright International Joint Conferences on Artificial Intelligence."}