{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2024,10,29]],"date-time":"2024-10-29T21:17:57Z","timestamp":1730236677349,"version":"3.28.0"},"reference-count":34,"publisher":"IEEE","license":[{"start":{"date-parts":[[2021,8,17]],"date-time":"2021-08-17T00:00:00Z","timestamp":1629158400000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/ieeexplore.ieee.org\/Xplorehelp\/downloads\/license-information\/IEEE.html"},{"start":{"date-parts":[[2021,8,17]],"date-time":"2021-08-17T00:00:00Z","timestamp":1629158400000},"content-version":"stm-asf","delay-in-days":0,"URL":"https:\/\/doi.org\/10.15223\/policy-029"},{"start":{"date-parts":[[2021,8,17]],"date-time":"2021-08-17T00:00:00Z","timestamp":1629158400000},"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":[[2021,8,17]]},"DOI":"10.1109\/iccse51940.2021.9569406","type":"proceedings-article","created":{"date-parts":[[2021,12,14]],"date-time":"2021-12-14T20:03:36Z","timestamp":1639512216000},"page":"518-523","source":"Crossref","is-referenced-by-count":2,"title":["PageRank Centrality based Graph Convolutional Networks for Semi-supervised Node Classification"],"prefix":"10.1109","author":[{"given":"Lei","family":"Chen","sequence":"first","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Junchi","family":"Liu","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Xueyuan","family":"Zhang","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Qifeng","family":"Zhou","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]}],"member":"263","reference":[{"key":"ref33","article-title":"Improving graph attention networks with large margin-based constraints","author":"wang","year":"2019","journal-title":"arXiv preprint arXiv 1910 10335"},{"key":"ref32","doi-asserted-by":"publisher","DOI":"10.1145\/265910.265914"},{"key":"ref31","doi-asserted-by":"publisher","DOI":"10.1145\/2623330.2623732"},{"key":"ref30","first-page":"40","article-title":"Revisiting semi-supervised learning with graph embeddings","author":"yang","year":"0","journal-title":"International conference on machine learning PMLR Conference Proceedings"},{"key":"ref34","first-page":"1725","article-title":"Simple and deep graph convolutional networks","author":"chen","year":"0","journal-title":"International conference on machine learning PMLR Conference Proceedings"},{"key":"ref10","article-title":"Graph attention networks","author":"veli?kovi?","year":"2017","journal-title":"arXiv preprint arXiv 1710 10903"},{"key":"ref11","first-page":"1025","article-title":"Inductive representation learning on large graphs","author":"hamilton","year":"0","journal-title":"Proceedings of the 31st International Conference on Neural Information Processing Systems Conference Proceedings"},{"key":"ref12","article-title":"Predict then propagate: Graph neural networks meet personalized pagerank","author":"klicpera","year":"2018","journal-title":"arXiv preprint arXiv 1810 04805"},{"key":"ref13","first-page":"6861","article-title":"Simplifying graph convolutional networks","author":"wu","year":"0","journal-title":"International conference on machine learning PMLR Conference Proceedings"},{"key":"ref14","first-page":"5453","article-title":"Representation learning on graphs with jumping knowledge networks","author":"xu","year":"0","journal-title":"International conference on machine learning PMLR Conference Proceedings"},{"key":"ref15","article-title":"Dropedge: Towards deep graph convolutional networks on node classification","author":"rong","year":"2019","journal-title":"arXiv preprint arXiv 1907 11634"},{"journal-title":"The PageRank Citation Ranking Bringing Order to the Web","year":"1999","author":"page","key":"ref16"},{"key":"ref17","article-title":"Inductive representation learning on temporal graphs","author":"xu","year":"2020","journal-title":"arXiv preprint arXiv 2002 05155"},{"key":"ref18","article-title":"Fastgcn: fast learning with graph convolutional networks via importance sampling","author":"chen","year":"2018","journal-title":"Arxiv preprint arXiv 1801 10588"},{"key":"ref19","article-title":"Adaptive sampling towards fast graph representation learning","author":"huang","year":"2018","journal-title":"arXiv preprint arXiv 1809 05343"},{"key":"ref28","article-title":"Adam: A method for stochastic optimization","author":"kingma","year":"2014","journal-title":"arXiv preprint arXiv 1412 6980"},{"key":"ref4","article-title":"Semi-supervised classification with graph convolutional networks","author":"kipf","year":"2016","journal-title":"arXiv preprint arXiv 1609 02907"},{"key":"ref27","doi-asserted-by":"publisher","DOI":"10.1080\/0022250X.2001.9990249"},{"key":"ref3","article-title":"Convolutional neural networks on graphs with fast localized spectral filtering","author":"defferrard","year":"2016","journal-title":"arXiv preprint arXiv 1606 09375"},{"key":"ref6","doi-asserted-by":"publisher","DOI":"10.1109\/TITS.2019.2935152"},{"key":"ref29","doi-asserted-by":"publisher","DOI":"10.1609\/aimag.v29i3.2157"},{"key":"ref5","doi-asserted-by":"publisher","DOI":"10.1145\/3289600.3290989"},{"key":"ref8","doi-asserted-by":"publisher","DOI":"10.18653\/v1\/D17-1209"},{"key":"ref7","article-title":"Pointnet++: Deep hierarchical feature learning on point sets in a metric space","author":"qi","year":"2017","journal-title":"arXiv preprint arXiv 1706 02413"},{"key":"ref2","article-title":"Spectral networks and locally connected networks on graphs","author":"bruna","year":"2013","journal-title":"arXiv preprint arXiv 1312 6203"},{"key":"ref9","article-title":"Deeper insights into graph convolutional networks for semi-supervised learning","volume":"32","author":"li","year":"0","journal-title":"Proceedings of the AAAI Conference on Artificial Intelligence"},{"key":"ref1","doi-asserted-by":"publisher","DOI":"10.1109\/5.726791"},{"key":"ref20","doi-asserted-by":"publisher","DOI":"10.1109\/ICCV.2019.00936"},{"key":"ref22","doi-asserted-by":"publisher","DOI":"10.1109\/CVPR.2017.243"},{"key":"ref21","doi-asserted-by":"publisher","DOI":"10.1109\/CVPR.2016.90"},{"key":"ref24","doi-asserted-by":"publisher","DOI":"10.1109\/TNN.2008.2005605"},{"key":"ref23","doi-asserted-by":"publisher","DOI":"10.1145\/3292500.3330925"},{"key":"ref26","doi-asserted-by":"publisher","DOI":"10.1086\/228631"},{"key":"ref25","article-title":"Improving neural networks by preventing co-adaptation of feature detectors","author":"hinton","year":"2012","journal-title":"arXiv preprint arXiv 1207 0580"}],"event":{"name":"2021 16th International Conference on Computer Science & Education (ICCSE)","start":{"date-parts":[[2021,8,17]]},"location":"Lancaster, United Kingdom","end":{"date-parts":[[2021,8,21]]}},"container-title":["2021 16th International Conference on Computer Science &amp; Education (ICCSE)"],"original-title":[],"link":[{"URL":"http:\/\/xplorestaging.ieee.org\/ielx7\/9569243\/9569247\/09569406.pdf?arnumber=9569406","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2022,5,10]],"date-time":"2022-05-10T15:47:30Z","timestamp":1652197650000},"score":1,"resource":{"primary":{"URL":"https:\/\/ieeexplore.ieee.org\/document\/9569406\/"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2021,8,17]]},"references-count":34,"URL":"https:\/\/doi.org\/10.1109\/iccse51940.2021.9569406","relation":{},"subject":[],"published":{"date-parts":[[2021,8,17]]}}}