{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2024,10,22]],"date-time":"2024-10-22T17:46:01Z","timestamp":1729619161327,"version":"3.28.0"},"reference-count":40,"publisher":"IEEE","license":[{"start":{"date-parts":[[2020,12,10]],"date-time":"2020-12-10T00:00:00Z","timestamp":1607558400000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/ieeexplore.ieee.org\/Xplorehelp\/downloads\/license-information\/IEEE.html"},{"start":{"date-parts":[[2020,12,10]],"date-time":"2020-12-10T00:00:00Z","timestamp":1607558400000},"content-version":"stm-asf","delay-in-days":0,"URL":"https:\/\/doi.org\/10.15223\/policy-029"},{"start":{"date-parts":[[2020,12,10]],"date-time":"2020-12-10T00:00:00Z","timestamp":1607558400000},"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":[[2020,12,10]]},"DOI":"10.1109\/bigdata50022.2020.9378219","type":"proceedings-article","created":{"date-parts":[[2021,3,19]],"date-time":"2021-03-19T21:10:21Z","timestamp":1616188221000},"page":"1455-1464","source":"Crossref","is-referenced-by-count":1,"title":["EGAD: Evolving Graph Representation Learning with Self-Attention and Knowledge Distillation for Live Video Streaming Events"],"prefix":"10.1109","author":[{"given":"Stefanos","family":"Antaris","sequence":"first","affiliation":[]},{"given":"Dimitrios","family":"Rafailidis","sequence":"additional","affiliation":[]},{"given":"Sarunas","family":"Girdzijauskas","sequence":"additional","affiliation":[]}],"member":"263","reference":[{"article-title":"Deepgraph: Graph structure predicts network growth","year":"2016","author":"li","key":"ref39"},{"key":"ref38","article-title":"Fast and accurate deep network learning by exponential linear units (elus)","author":"clevert","year":"2016","journal-title":"ICLRE"},{"key":"ref33","article-title":"Dyngem: Deep embedding method for dynamic graphs","volume":"abs 1805 11273","author":"goyal","year":"2018"},{"key":"ref32","first-page":"571","article-title":"Dynamic network embedding by modeling triadic closure process","author":"zhou","year":"2018","journal-title":"AAAI"},{"key":"ref31","first-page":"141","article-title":"Graph-based knowledge distillation by multi-head attention network","author":"lee","year":"2019","journal-title":"BMVC"},{"key":"ref30","doi-asserted-by":"publisher","DOI":"10.1109\/ICASSP40776.2020.9053986"},{"key":"ref37","article-title":"VStreamDRLS: Dynamic graph representationlearning with self-attention for enterprisedistributed video streaming solutions","author":"antaris","year":"2020","journal-title":"ASONAM"},{"key":"ref36","first-page":"362","article-title":"Structured sequence modeling with graph convolutional recurrent networks","author":"seo","year":"2018","journal-title":"ICONIP"},{"key":"ref35","article-title":"Semi-supervised classification with graph convolutional networks","author":"kipf","year":"2017","journal-title":"ICLRE"},{"key":"ref34","article-title":"Large scale distributed neural network training through online distillation","author":"anil","year":"2018","journal-title":"ICLRE"},{"key":"ref10","doi-asserted-by":"publisher","DOI":"10.1145\/2623330.2623732"},{"key":"ref40","doi-asserted-by":"publisher","DOI":"10.18653\/v1\/D16-1139"},{"key":"ref11","doi-asserted-by":"publisher","DOI":"10.1145\/3336191.3371845"},{"key":"ref12","doi-asserted-by":"publisher","DOI":"10.1609\/aaai.v34i04.5984"},{"key":"ref13","first-page":"10711","article-title":"Semi-implicit graph variational auto-encoders","author":"hasanzadeh","year":"2019","journal-title":"NeurIPS"},{"key":"ref14","first-page":"385","article-title":"Dynamic joint variational graph autoencoders","author":"mahdavi","year":"2019","journal-title":"ECML"},{"key":"ref15","first-page":"1024","article-title":"Inductive representation learning on large graphs","author":"hamilton","year":"2017","journal-title":"NIPS"},{"key":"ref16","article-title":"Graph attention networks","author":"velickovic","year":"2018","journal-title":"ICLRE"},{"key":"ref17","doi-asserted-by":"publisher","DOI":"10.1145\/2806416.2806512"},{"key":"ref18","doi-asserted-by":"publisher","DOI":"10.1145\/2939672.2939753"},{"key":"ref19","doi-asserted-by":"crossref","DOI":"10.1609\/icwsm.v14i1.7305","article-title":"Gossip and attend: Context-sensitive graph representation learning","author":"kefato","year":"2020","journal-title":"ICWSM"},{"key":"ref28","first-page":"5142","article-title":"Towards understanding knowledge distillation","author":"phuong","year":"2019","journal-title":"ICML"},{"key":"ref4","doi-asserted-by":"publisher","DOI":"10.1145\/2486001.2491685"},{"key":"ref27","doi-asserted-by":"publisher","DOI":"10.1145\/1150402.1150464"},{"key":"ref3","doi-asserted-by":"publisher","DOI":"10.1145\/2713168.2713182"},{"key":"ref6","doi-asserted-by":"publisher","DOI":"10.1145\/3332186.3333045"},{"key":"ref29","article-title":"Graph representation learning via multi-task knowledge distillation","author":"ma","year":"2019","journal-title":"NeurIPS"},{"key":"ref5","first-page":"1","article-title":"Destinationaware adaptive traffic flow rule aggregation in software-defined networks","author":"phan","year":"2019","journal-title":"NetSys"},{"key":"ref8","doi-asserted-by":"publisher","DOI":"10.1145\/2939672.2939754"},{"year":"2016","key":"ref7","article-title":"GDPR Regulation Europe"},{"key":"ref2","doi-asserted-by":"publisher","DOI":"10.1016\/j.comnet.2017.05.030"},{"key":"ref9","first-page":"52","article-title":"Representation learning on graphs: Methods and applications","volume":"40","author":"hamilton","year":"2017","journal-title":"IEEE Data Eng Bull"},{"key":"ref1","doi-asserted-by":"publisher","DOI":"10.1016\/j.compeleceng.2017.04.011"},{"key":"ref20","doi-asserted-by":"publisher","DOI":"10.1016\/j.knosys.2019.06.024"},{"key":"ref22","first-page":"2654","article-title":"Do deep nets really need to be deep?","author":"ba","year":"2014","journal-title":"NIPS"},{"key":"ref21","doi-asserted-by":"publisher","DOI":"10.1145\/3219819.3220021"},{"key":"ref24","doi-asserted-by":"publisher","DOI":"10.1109\/CVPR.2019.00726"},{"key":"ref23","article-title":"Distilling the knowledge in a neural network","author":"hinton","year":"2015","journal-title":"NIPS"},{"key":"ref26","doi-asserted-by":"publisher","DOI":"10.1109\/CVPR.2017.754"},{"key":"ref25","doi-asserted-by":"publisher","DOI":"10.1109\/TVCG.2019.2903943"}],"event":{"name":"2020 IEEE International Conference on Big Data (Big Data)","start":{"date-parts":[[2020,12,10]]},"location":"Atlanta, GA, USA","end":{"date-parts":[[2020,12,13]]}},"container-title":["2020 IEEE International Conference on Big Data (Big Data)"],"original-title":[],"link":[{"URL":"http:\/\/xplorestaging.ieee.org\/ielx7\/9377717\/9377728\/09378219.pdf?arnumber=9378219","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2023,10,23]],"date-time":"2023-10-23T14:37:12Z","timestamp":1698071832000},"score":1,"resource":{"primary":{"URL":"https:\/\/ieeexplore.ieee.org\/document\/9378219\/"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2020,12,10]]},"references-count":40,"URL":"https:\/\/doi.org\/10.1109\/bigdata50022.2020.9378219","relation":{},"subject":[],"published":{"date-parts":[[2020,12,10]]}}}