{"type":"ScholarlyArticle","title":"Large Scale Evolving Graphs with Burst Detection","authors":[{"name":"Yifeng Zhao","slug":"yifeng-zhao","given":"Yifeng","family":"Zhao","affiliation":"Department of Computer Science and Technology , Tsinghua University 2 DAMO Academy , Alibaba"},{"name":"Xiangwei Wang","slug":"xiangwei-wang","given":"Xiangwei","family":"Wang","affiliation":"Group"},{"name":"Hongxia Yang","slug":"hongxia-yang","given":"Hongxia","family":"Yang","affiliation":"Group"},{"name":"Le Song","slug":"le-song","given":"Le","family":"Song","affiliation":"Ant Financial"},{"name":"Jie Tang","slug":"jie-tang","given":"Jie","family":"Tang","affiliation":"Department of Computer Science and Technology , Tsinghua University 2 DAMO Academy , Alibaba"}],"abstract":"Analyzing large-scale evolving graphs are crucial for understanding the dynamic and evolutionary nature of social networks. Most existing works focus on discovering repeated and consistent temporal patterns, however, such patterns cannot fully explain the complexity observed in dynamic networks. For example, in recommendation scenarios, users sometimes purchase products on a whim during a window shopping.Thus, in this paper, we design and implement a novel framework called BurstGraph which can capture both recurrent and consistent patterns, and especially unexpected bursty network changes. The performance of the proposed algorithm is demonstrated on both a simulated dataset and a world-leading E-Commerce company dataset, showing that they are able to discriminate recurrent events from extremely bursty events in terms of action propensity.","doi":"10.24963/ijcai.2019/613","url":"https://ijcai.org/proceedings/2019/613","pdf":"https://ijcai.org/proceedings/2019/0613.pdf","markdown":"https://ijcai.org/proceedings/2019/613.md","bibtex":"https://ijcai.org/proceedings/2019/bibtex/613","volume":{"slug":"2019","title":"Proceedings of the Twenty-Eighth International Joint Conference on Artificial Intelligence","short_title":"IJCAI 2019","url":"https://ijcai.org/proceedings/2019/","year":2019,"location":"Macao","dates":"10-16 August 2019","editors":"Sarit Kraus","isbn":"978-0-9992411-4-1","publisher":"International Joint Conferences on Artificial Intelligence Organization","doi_prefix":"10.24963/ijcai.2019"},"section":"Machine Learning M-Z","track":"Main track","number":613,"pages":{"first":4412,"last":4418},"keywords":["Machine Learning Applications: Applications of Supervised Learning","Machine Learning: Deep Learning","Machine Learning: Classification","Machine Learning: Time-series;Data Streams"],"videos":[],"references":[{"position":1,"raw":"[Akoglu and Faloutsos, 2013] Leman Akoglu and Christos Faloutsos. Anomaly, event, and fraud detection in large network datasets. In WSDM, pages 773-774. ACM, 2013.","title":"Leman Akoglu and Christos Faloutsos. Anomaly, event, and fraud detection in large network datasets","authors":"Faloutsos Akoglu","year":"2013","venue":"WSDM","doi":null,"url":null,"internal_url":null},{"position":2,"raw":"[Akoglu et al., 2015] Leman Akoglu, Hanghang Tong, and Danai Koutra. Graph based anomaly detection and descrip- tion: a survey. In Data Mining and Knowledge Discovery 29, volume 3, pages 626-688. ACM, 2015.","title":"Graph based anomaly detection and description: a survey","authors":"Akoglu","year":"2015","venue":"Data Mining and Knowledge Discovery","doi":null,"url":null,"internal_url":null},{"position":3,"raw":"[Angel et al., 2012] Albert Angel, Nikos Sarkas, Nick Koudas, and Divesh Srivastava. Dense subgraph main- tenance under streaming edge weight updates for real-time story identification. Proc. VLDB Endow., 5(6):574-585, 2012.","title":"Dense subgraph maintenance under streaming edge weight updates for real-time story identification","authors":"Albert Angel, Nikos Sarkas, Nick Koudas, Divesh Srivastava","year":"2012","venue":"Proc. VLDB Endow","doi":null,"url":null,"internal_url":null},{"position":4,"raw":"[Chandola et al., 2009] Varun Chandola, Arindam Banerjee, and Vipin Kumar. Anomaly detection: A survey. ACM Comput. Surv., 41(3):15:1-15:58, July 2009.","title":"Anomaly detection: A survey","authors":"Varun Chandola, Arindam Banerjee, Vipin Kumar","year":"2009","venue":"ACM Comput. Surv","doi":null,"url":null,"internal_url":null},{"position":5,"raw":"[Dai et al., 2016] Hanjun Dai, Bo Dai, and Le Song. Discrim- inative embeddings of latent variable models for structured data. In International conference on machine learning, pages 2702-2711, 2016.","title":"Discriminative embeddings of latent variable models for structured data","authors":"Dai","year":"2016","venue":"International conference on machine learning","doi":null,"url":null,"internal_url":null},{"position":6,"raw":"[Dong et al., 2017] Yuxiao Dong, Nitesh V Chawla, and Ananthram Swami. metapath2vec: Scalable representation learning for heterogeneous networks. In Proceedings of the 23rd ACM SIGKDD international conference on knowledge discovery and data mining, pages 135-144. ACM, 2017.","title":"metapath2vec: Scalable representation learning for heterogeneous networks","authors":"Dong","year":"2017","venue":"Proceedings of the 23rd ACM SIGKDD international conference on knowledge discovery and data mining","doi":null,"url":null,"internal_url":null},{"position":7,"raw":"[Du et al., 2018] Lun Du, Yun Wang, Guojie Song, Zhicong Lu, and Junshan Wang. Dynamic network embedding: An extended approach for skip-gram based network embed- ding. In IJCAI, pages 2086-2092, 2018.","title":"Dynamic network embedding: An extended approach for skip-gram based network embedding","authors":"Lun Du, Yun Wang, Guojie Song, Zhicong Lu, Junshan Wang","year":"2018","venue":"IJCAI","doi":null,"url":null,"internal_url":"/proceedings/2018/288"},{"position":8,"raw":"[Grover and Leskovec, 2016] Aditya Grover and Jure Leskovec. node2vec: Scalable feature learning for networks. In Proceedings of the 22nd ACM SIGKDD international conference on Knowledge discovery and data mining, pages 855-864. ACM, 2016.","title":"node2vec: Scalable feature learning for networks","authors":"Leskovec Grover, Aditya Grover, Jure Leskovec","year":"2016","venue":"Proceedings of the 22nd ACM SIGKDD international conference on Knowledge discovery and data mining","doi":null,"url":null,"internal_url":null},{"position":9,"raw":"[Hamilton et al., 2017a] Will Hamilton, Zhitao Ying, and Jure Leskovec. Inductive representation learning on large graphs. In Advances in Neural Information Processing Systems, pages 1024-1034, 2017.","title":"Inductive representation learning on large graphs","authors":"Hamilton","year":"2017","venue":"Advances in Neural Information Processing Systems","doi":null,"url":null,"internal_url":null},{"position":10,"raw":"[Hamilton et al., 2017b] William L. Hamilton, Rex Ying, and Jure Leskovec. Inductive representation learning on large graphs. In NIPS, 2017.","title":"Inductive representation learning on large graphs","authors":"Hamilton","year":"2017","venue":"NIPS","doi":null,"url":null,"internal_url":null},{"position":11,"raw":"[Heard et al., 2010] Nicholas A Heard, David J Weston, Kiri- aki Platanioti, David J Hand, et al. Bayesian anomaly de- tection methods for social networks. The Annals of Applied Statistics, 4(2):645-662, 2010.","title":"Bayesian anomaly detection methods for social networks","authors":"Nicholas A Heard, David J Weston, Kiriaki Platanioti, David J Hand","year":"2010","venue":"The Annals of Applied Statistics","doi":null,"url":null,"internal_url":null},{"position":12,"raw":"[Ishwaran et al., 2005] Hemant Ishwaran, J Sunil Rao, et al. Spike and slab variable selection: frequentist and bayesian strategies. The Annals of Statistics, 33(2):730-773, 2005.","title":"Spike and slab variable selection: frequentist and bayesian strategies","authors":", J Sunil Hemant Ishwaran, Rao","year":"2005","venue":"The Annals of Statistics","doi":null,"url":null,"internal_url":null},{"position":13,"raw":"[Kingma and Ba, 2014] Diederik P Kingma and Jimmy Ba. Adam: A method for stochastic optimization. arXiv preprint arXiv:1412.6980, 2014.","title":"Adam: A method for stochastic optimization","authors":"P Diederik, Jimmy Kingma, Ba","year":"2014","venue":null,"doi":null,"url":null,"internal_url":null},{"position":14,"raw":"[Kingma and Welling, 2013] Diederik P Kingma and Max Welling. Auto-encoding variational bayes. arXiv preprint arXiv:1312.6114, 2013.","title":"Auto-encoding variational bayes","authors":"P Diederik, Max Kingma, Welling","year":"2013","venue":null,"doi":null,"url":null,"internal_url":null},{"position":15,"raw":"[Kleinberg, 2003] Jon Kleinberg. Bursty and hierarchical structure in streams. Data Mining and Knowledge Discov- ery, 7(4):373-397, 2003.","title":"Bursty and hierarchical structure in streams","authors":"Jon Kleinberg, Kleinberg","year":"2003","venue":"Data Mining and Knowledge Discovery","doi":null,"url":null,"internal_url":null},{"position":16,"raw":"[Li et al., 2017] Jundong Li, Harsh Dani, Xia Hu, Jiliang Tang, Yi Chang, and Huan Liu. Attributed network embed- ding for learning in a dynamic environment. In Proceedings of the 2017 ACM on Conference on Information and Knowl- edge Management, pages 387-396. ACM, 2017.","title":"Attributed network embedding for learning in a dynamic environment","authors":"Jundong Li, Harsh Dani, Xia Hu, Jiliang Tang, Yi Chang, Huan Liu","year":"2017","venue":"Proceedings of the 2017 ACM on Conference on Information and Knowledge Management","doi":null,"url":null,"internal_url":null},{"position":17,"raw":"[Mikolov et al., 2013] Tomas Mikolov, Ilya Sutskever, Kai Chen, Greg S Corrado, and Jeff Dean. Distributed represen- tations of words and phrases and their compositionality. In Advances in neural information processing systems, pages 3111-3119, 2013.","title":"Distributed representations of words and phrases and their compositionality","authors":"Mikolov","year":"2013","venue":"Advances in neural information processing systems","doi":null,"url":null,"internal_url":null},{"position":18,"raw":"[Mitchell and Beauchamp, 1988] Toby J Mitchell and John J Beauchamp. Bayesian variable selection in linear regres- sion. Journal of the American Statistical Association, 83(404):1023-1032, 1988.","title":"Bayesian variable selection in linear regression","authors":"J Toby, John J Mitchell, Beauchamp","year":"1988","venue":"Journal of the American Statistical Association","doi":null,"url":null,"internal_url":null},{"position":19,"raw":"[Nguyen et al., 2018] Giang Hoang Nguyen, John Boaz Lee, Ryan A Rossi, Nesreen K Ahmed, Eunyee Koh, and Sungchul Kim. Continuous-time dynamic network em- beddings. In Companion of the The Web Conference 2018 on The Web Conference 2018, pages 969-976. International World Wide Web Conferences Steering Committee, 2018.","title":"Continuous-time dynamic network embeddings","authors":"Giang Hoang Nguyen, John Boaz Lee, Ryan A Rossi, Nesreen K Ahmed, Eunyee Koh, Sungchul Kim","year":"2018","venue":"Companion of the The Web Conference 2018 on The Web Conference 2018","doi":null,"url":null,"internal_url":null},{"position":20,"raw":"[Parikh and Sundaresan, 2008] Nish Parikh and Neel Sun- daresan. Scalable and near real-time burst detection from ecommerce queries. In Proceedings of the 14th ACM SIGKDD international conference on Knowledge discovery and data mining, pages 972-980. ACM, 2008.","title":"Scalable and near real-time burst detection from ecommerce queries","authors":"Nish Parikh, Neel Sundaresan","year":"2008","venue":"Proceedings of the 14th ACM SIGKDD international conference on Knowledge discovery and data mining","doi":null,"url":null,"internal_url":null},{"position":21,"raw":"[Perozzi et al., 2014] Bryan Perozzi, Rami Al-Rfou, and Steven Skiena. Deepwalk: Online learning of social rep- resentations. In Proceedings of the 20th ACM SIGKDD international conference on Knowledge discovery and data mining, pages 701-710. ACM, 2014.","title":"Deepwalk: Online learning of social representations","authors":"Bryan Perozzi, Rami Al-Rfou, Steven Skiena","year":"2014","venue":"Proceedings of the 20th ACM SIGKDD international conference on Knowledge discovery and data mining","doi":null,"url":null,"internal_url":null},{"position":22,"raw":"[Qiu et al., 2018] Jiezhong Qiu, Yuxiao Dong, Hao Ma, Jian Li, Kuansan Wang, and Jie Tang. Network embedding as matrix factorization: Unifying deepwalk, line, pte, and node2vec. In WSDM, pages 459-467. ACM, 2018.","title":"Network embedding as matrix factorization: Unifying deepwalk, line, pte, and node2vec","authors":"Qiu","year":"2018","venue":"WSDM","doi":null,"url":null,"internal_url":null},{"position":23,"raw":"[Trivedi et al., 2017] Rakshit Trivedi, Hanjun Dai, Yichen Wang, and Le Song. Know-evolve: Deep temporal rea- soning for dynamic knowledge graphs. In Proceedings of the 34th International Conference on Machine Learning- Volume 70, pages 3462-3471. JMLR. org, 2017.","title":"Know-evolve: Deep temporal reasoning for dynamic knowledge graphs","authors":"Rakshit Trivedi, Hanjun Dai, Yichen Wang, Le Song","year":"2017","venue":"Proceedings of the 34th International Conference on Machine Learning","doi":null,"url":null,"internal_url":null},{"position":24,"raw":"[Wang et al., 2016] Daixin Wang, Peng Cui, and Wenwu Zhu. Structural deep network embedding. In KDD, pages 1225- 1234. ACM, 2016.","title":"Structural deep network embedding","authors":"Wang","year":"2016","venue":"KDD","doi":null,"url":null,"internal_url":null},{"position":25,"raw":"[Zhou et al., 2018] Lekui Zhou, Yang Yang, Xiang Ren, Fei Wu, and Yueting Zhuang. Dynamic network embedding by modeling triadic closure process. 2018.","title":"Dynamic network embedding by modeling triadic closure process","authors":"Lekui Zhou, Yang Yang, Xiang Ren, Fei Wu, Yueting Zhuang","year":"2018","venue":null,"doi":null,"url":null,"internal_url":null},{"position":26,"raw":"[Zhu et al., 2016] Linhong Zhu, Dong Guo, Junming Yin, Greg Ver Steeg, and Aram Galstyan. Scalable temporal latent space inference for link prediction in dynamic so- cial networks. IEEE Transactions on Knowledge and Data Engineering, 28(10):2765-2777, 2016.","title":"Scalable temporal latent space inference for link prediction in dynamic social networks","authors":"Zhu","year":"2016","venue":"IEEE Transactions on Knowledge and Data Engineering","doi":null,"url":null,"internal_url":null}],"license_note":"Copyright International Joint Conferences on Artificial Intelligence."}