{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,3,8]],"date-time":"2026-03-08T00:55:55Z","timestamp":1772931355749,"version":"3.50.1"},"reference-count":32,"publisher":"IEEE","license":[{"start":{"date-parts":[[2022,7,18]],"date-time":"2022-07-18T00:00:00Z","timestamp":1658102400000},"content-version":"stm-asf","delay-in-days":0,"URL":"https:\/\/doi.org\/10.15223\/policy-029"},{"start":{"date-parts":[[2022,7,18]],"date-time":"2022-07-18T00:00:00Z","timestamp":1658102400000},"content-version":"stm-asf","delay-in-days":0,"URL":"https:\/\/doi.org\/10.15223\/policy-037"}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":[],"published-print":{"date-parts":[[2022,7,18]]},"DOI":"10.1109\/ijcnn55064.2022.9892872","type":"proceedings-article","created":{"date-parts":[[2022,9,30]],"date-time":"2022-09-30T19:56:04Z","timestamp":1664567764000},"page":"1-8","source":"Crossref","is-referenced-by-count":5,"title":["HE-SNE: Heterogeneous Event Sequence-based Streaming Network Embedding for Dynamic Behaviors"],"prefix":"10.1109","author":[{"given":"Yifan","family":"Wang","sequence":"first","affiliation":[{"name":"Peking University,School of Computer Science,Beijing,China"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Jianhao","family":"Shen","sequence":"additional","affiliation":[{"name":"Peking University,School of Computer Science,Beijing,China"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Yiping","family":"Song","sequence":"additional","affiliation":[{"name":"National University of Defense Technology,College of Liberal Arts and Sciences,Changsha,China"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Sheng","family":"Wang","sequence":"additional","affiliation":[{"name":"University of Washington,Paul G. Allen School of Computer Science,Seattle,WA"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Ming","family":"Zhang","sequence":"additional","affiliation":[{"name":"Peking University,School of Computer Science,Beijing,China"}],"role":[{"role":"author","vocabulary":"crossref"}]}],"member":"263","reference":[{"key":"ref32","first-page":"2579","volume":"9","author":"maaten","year":"2008","journal-title":"Visualizing Data using t-SNE Journal of Machine Learning Research"},{"key":"ref31","article-title":"Adam: A method for stochastic optimization","author":"kingma","year":"2014","journal-title":"International Conference on Learning Representations"},{"key":"ref30","volume":"5","author":"jaeger","year":"2002","journal-title":"Tutorial on training recurrent neural networks covering BPPT RTRL EKF and the” echo state network” approach"},{"key":"ref10","article-title":"DynGEM: Deep Em-bedding Method for Dynamic Graphs","author":"goyal","year":"2017","journal-title":"IJCAI International Workshop on Representation Learning for Graphs (ReLiG)"},{"key":"ref11","doi-asserted-by":"publisher","DOI":"10.1609\/aaai.v32i1.11257"},{"key":"ref12","doi-asserted-by":"publisher","DOI":"10.24963\/ijcai.2021\/203"},{"key":"ref13","doi-asserted-by":"publisher","DOI":"10.1145\/3184558.3191526"},{"key":"ref14","doi-asserted-by":"publisher","DOI":"10.1145\/3219819.3220054"},{"key":"ref15","first-page":"3462","article-title":"Knowevolve: Deep temporal reasoning for dynamic knowledge graphs","author":"trivedi","year":"2017","journal-title":"Proceedings of the 34th International Conference on Machine Learning"},{"key":"ref16","doi-asserted-by":"publisher","DOI":"10.1145\/3219819.3219949"},{"key":"ref17","article-title":"Entity embedding-based anomaly detection for heterogeneous categorical events","author":"chen","year":"2016","journal-title":"Proceedings of the 25th International Joint Conference on Artificial Intelligence"},{"key":"ref18","doi-asserted-by":"publisher","DOI":"10.1109\/TKDE.2017.2733530"},{"key":"ref19","doi-asserted-by":"publisher","DOI":"10.1145\/2623330.2623732"},{"key":"ref28","doi-asserted-by":"publisher","DOI":"10.1117\/12.280797"},{"key":"ref4","doi-asserted-by":"publisher","DOI":"10.1145\/2783258.2783348"},{"key":"ref27","doi-asserted-by":"publisher","DOI":"10.1145\/3292500.3330895"},{"key":"ref3","doi-asserted-by":"publisher","DOI":"10.1145\/3132847.3132919"},{"key":"ref6","article-title":"Deep Markov Factor Analysis: Towards Concurrent Temporal and Spatial Analysis of fMRI Data","volume":"34","author":"farnoosh","year":"2021","journal-title":"In Advances in Neural Information Processing Systems"},{"key":"ref29","doi-asserted-by":"publisher","DOI":"10.24963\/ijcai.2017\/504"},{"key":"ref5","doi-asserted-by":"publisher","DOI":"10.1109\/TPAMI.2021.3066551"},{"key":"ref8","first-page":"1797","author":"fu","year":"2017","journal-title":"Hin2vec Explore meta-paths in heterogeneous information networks for representation learning"},{"key":"ref7","doi-asserted-by":"publisher","DOI":"10.1145\/3097983.3098036"},{"key":"ref2","doi-asserted-by":"publisher","DOI":"10.1145\/2623330.2623644"},{"key":"ref9","doi-asserted-by":"publisher","DOI":"10.1145\/3340531.3411996"},{"key":"ref1","volume":"11","author":"zhang","year":"2016","journal-title":"A multiscale survival process for modeling human activity patterns PloS one"},{"key":"ref20","doi-asserted-by":"publisher","DOI":"10.1145\/2736277.2741093"},{"key":"ref22","doi-asserted-by":"publisher","DOI":"10.1145\/3308558.3313562"},{"key":"ref21","first-page":"855","article-title":"2016. node2vec: Scalable feature learning for networks","author":"grover","year":"0","journal-title":"ACM SIGKDD International Conference on Knowledge Discovery and Data Mining"},{"key":"ref24","article-title":"Gated graph sequence neural networks","author":"li","year":"2016","journal-title":"International Conference on Learning Representations"},{"key":"ref23","doi-asserted-by":"publisher","DOI":"10.1109\/TNN.2008.2005605"},{"key":"ref26","first-page":"4547","article-title":"Coevolutionary latent feature processes for continuous-time useritem interactions","author":"wang","year":"2016","journal-title":"Advances in neural information processing systems"},{"key":"ref25","doi-asserted-by":"publisher","DOI":"10.24963\/ijcai.2018\/288"}],"event":{"name":"2022 International Joint Conference on Neural Networks (IJCNN)","location":"Padua, Italy","start":{"date-parts":[[2022,7,18]]},"end":{"date-parts":[[2022,7,23]]}},"container-title":["2022 International Joint Conference on Neural Networks (IJCNN)"],"original-title":[],"link":[{"URL":"http:\/\/xplorestaging.ieee.org\/ielx7\/9891857\/9889787\/09892872.pdf?arnumber=9892872","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2022,11,3]],"date-time":"2022-11-03T22:56:35Z","timestamp":1667516195000},"score":1,"resource":{"primary":{"URL":"https:\/\/ieeexplore.ieee.org\/document\/9892872\/"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2022,7,18]]},"references-count":32,"URL":"https:\/\/doi.org\/10.1109\/ijcnn55064.2022.9892872","relation":{},"subject":[],"published":{"date-parts":[[2022,7,18]]}}}