{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2025,12,26]],"date-time":"2025-12-26T07:11:04Z","timestamp":1766733064627,"version":"3.37.3"},"reference-count":37,"publisher":"IEEE","license":[{"start":{"date-parts":[[2022,11,10]],"date-time":"2022-11-10T00:00:00Z","timestamp":1668038400000},"content-version":"stm-asf","delay-in-days":0,"URL":"https:\/\/doi.org\/10.15223\/policy-029"},{"start":{"date-parts":[[2022,11,10]],"date-time":"2022-11-10T00:00:00Z","timestamp":1668038400000},"content-version":"stm-asf","delay-in-days":0,"URL":"https:\/\/doi.org\/10.15223\/policy-037"}],"funder":[{"DOI":"10.13039\/100000001","name":"NSF","doi-asserted-by":"publisher","award":["2007793,2034850,2131509"],"award-info":[{"award-number":["2007793,2034850,2131509"]}],"id":[{"id":"10.13039\/100000001","id-type":"DOI","asserted-by":"publisher"}]}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":[],"published-print":{"date-parts":[[2022,11,10]]},"DOI":"10.1109\/asonam55673.2022.10068646","type":"proceedings-article","created":{"date-parts":[[2023,3,23]],"date-time":"2023-03-23T17:38:09Z","timestamp":1679593089000},"page":"88-95","source":"Crossref","is-referenced-by-count":2,"title":["Deep Graph Clustering with Random-walk based Scalable Learning"],"prefix":"10.1109","author":[{"given":"Xiang","family":"Li","sequence":"first","affiliation":[{"name":"The Ohio State University,Computer Science and Engineering"}]},{"given":"Dong","family":"Li","sequence":"additional","affiliation":[{"name":"Kent State University,Computer Science Department"}]},{"given":"Ruoming","family":"Jin","sequence":"additional","affiliation":[{"name":"Kent State University,Computer Science Department"}]},{"given":"Rajiv","family":"Ramnath","sequence":"additional","affiliation":[{"name":"The Ohio State University,Computer Science and Engineering"}]},{"given":"Gagan","family":"Agrawal","sequence":"additional","affiliation":[{"name":"Augusta University,Computer and Cyber Sciences"}]}],"member":"263","reference":[{"key":"ref1","article-title":"A k-means clustering algorithm","author":"Hartigan","year":"1979","journal-title":"JSTOR: Applied Statistics"},{"key":"ref2","doi-asserted-by":"publisher","DOI":"10.1016\/j.cosrev.2021.100379"},{"key":"ref3","article-title":"Semi-supervised classification with graph convolutional networks","author":"Thomas","year":"2017","journal-title":"ICLR"},{"key":"ref4","doi-asserted-by":"publisher","DOI":"10.1016\/j.aiopen.2021.01.001"},{"article-title":"Variational graph auto-encoders","volume-title":"NIPS Workshop on Bayesian Deep Learning","author":"Thomas","key":"ref5"},{"key":"ref6","article-title":"Deep Graph Infomax","author":"Velickovic","year":"2019","journal-title":"ICLR"},{"key":"ref7","doi-asserted-by":"publisher","DOI":"10.1145\/3366423.3380214"},{"key":"ref8","doi-asserted-by":"publisher","DOI":"10.1609\/aaai.v32i1.11604"},{"key":"ref9","article-title":"Simplifying graph convolutional networks","author":"Wu","year":"2019","journal-title":"ICML"},{"key":"ref10","doi-asserted-by":"publisher","DOI":"10.24963\/ijcai.2019\/601"},{"key":"ref11","article-title":"Simple spectral graph convolution","author":"Zhu","year":"2021","journal-title":"ICLR"},{"key":"ref12","article-title":"Optimizing generalized pagerank methods for seed-expansion community detection","author":"Li","year":"2019","journal-title":"NIPS"},{"key":"ref13","article-title":"Scalable graph neural networks via bidirectional propagation","author":"Chen","year":"2020","journal-title":"NeurIPS"},{"key":"ref14","doi-asserted-by":"publisher","DOI":"10.3390\/technologies9010002"},{"key":"ref15","doi-asserted-by":"publisher","DOI":"10.24963\/ijcai.2018\/362"},{"key":"ref16","doi-asserted-by":"publisher","DOI":"10.1145\/3132847.3132967"},{"journal-title":"Graph clustering with graph neural networks","year":"2020","author":"Tsitsulin","key":"ref17"},{"key":"ref18","doi-asserted-by":"publisher","DOI":"10.1145\/3596711.3596724"},{"key":"ref19","article-title":"Predict then propagate: Graph neural networks meet personalized pagerank","author":"Klicpera","year":"2019","journal-title":"ICLR"},{"key":"ref20","article-title":"Diffusion improves graph learning","author":"Klicpera","year":"2019","journal-title":"NeurIPS"},{"key":"ref21","doi-asserted-by":"publisher","DOI":"10.24963\/ijcai.2019\/509"},{"key":"ref22","article-title":"Visualizing data using t-SNE","author":"Van Der Maaten","year":"2008","journal-title":"JMLR"},{"key":"ref23","doi-asserted-by":"publisher","DOI":"10.1090\/cbms\/092"},{"key":"ref24","article-title":"Representation learning on graphs with jumping knowledge networks","author":"Xu","year":"2018","journal-title":"ICML"},{"key":"ref25","article-title":"The pagerank citation ranking: Bringing order to the web","author":"Page","year":"1999","journal-title":"WWW"},{"key":"ref26","doi-asserted-by":"publisher","DOI":"10.1609\/aimag.v29i3.2157"},{"journal-title":"Pitfalls of graph neural network evaluation","year":"2019","author":"Shchur","key":"ref27"},{"key":"ref28","doi-asserted-by":"publisher","DOI":"10.1145\/2350190.2350193"},{"key":"ref29","doi-asserted-by":"publisher","DOI":"10.1145\/2623330.2623732"},{"key":"ref30","doi-asserted-by":"publisher","DOI":"10.1073\/pnas.0601602103"},{"key":"ref31","article-title":"Scalable simd-efficient graph processing on gpus","author":"Farzad","year":"2015","journal-title":"PACT"},{"key":"ref32","doi-asserted-by":"publisher","DOI":"10.1145\/1772690.1772755"},{"key":"ref33","doi-asserted-by":"publisher","DOI":"10.1145\/2623330.2623706"},{"key":"ref34","doi-asserted-by":"publisher","DOI":"10.1145\/3299869.3319886"},{"key":"ref35","article-title":"Accelerating t-sne using tree-based algorithms","author":"Van Der Maaten","year":"2014","journal-title":"J. Mach. Learn. Res."},{"journal-title":"Decoupled weight decay regularization","year":"2019","author":"Loshchilov","key":"ref36"},{"key":"ref37","article-title":"Scalable graph neural networks via bidirectional propagation","author":"Chen","year":"2020","journal-title":"NeurIPS"}],"event":{"name":"2022 IEEE\/ACM International Conference on Advances in Social Networks Analysis and Mining (ASONAM)","start":{"date-parts":[[2022,11,10]]},"location":"Istanbul, Turkey","end":{"date-parts":[[2022,11,13]]}},"container-title":["2022 IEEE\/ACM International Conference on Advances in Social Networks Analysis and Mining (ASONAM)"],"original-title":[],"link":[{"URL":"http:\/\/xplorestaging.ieee.org\/ielx7\/10068562\/10068565\/10068646.pdf?arnumber=10068646","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2024,3,14]],"date-time":"2024-03-14T05:55:23Z","timestamp":1710395723000},"score":1,"resource":{"primary":{"URL":"https:\/\/ieeexplore.ieee.org\/document\/10068646\/"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2022,11,10]]},"references-count":37,"URL":"https:\/\/doi.org\/10.1109\/asonam55673.2022.10068646","relation":{},"subject":[],"published":{"date-parts":[[2022,11,10]]}}}