{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,4,22]],"date-time":"2026-04-22T18:47:08Z","timestamp":1776883628828,"version":"3.51.2"},"reference-count":36,"publisher":"IEEE","license":[{"start":{"date-parts":[[2023,6,4]],"date-time":"2023-06-04T00:00:00Z","timestamp":1685836800000},"content-version":"stm-asf","delay-in-days":0,"URL":"https:\/\/doi.org\/10.15223\/policy-029"},{"start":{"date-parts":[[2023,6,4]],"date-time":"2023-06-04T00:00:00Z","timestamp":1685836800000},"content-version":"stm-asf","delay-in-days":0,"URL":"https:\/\/doi.org\/10.15223\/policy-037"}],"funder":[{"DOI":"10.13039\/501100001809","name":"National Natural Science Foundation of China","doi-asserted-by":"publisher","id":[{"id":"10.13039\/501100001809","id-type":"DOI","asserted-by":"publisher"}]}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":[],"published-print":{"date-parts":[[2023,6,4]]},"DOI":"10.1109\/icassp49357.2023.10094812","type":"proceedings-article","created":{"date-parts":[[2023,5,5]],"date-time":"2023-05-05T17:28:30Z","timestamp":1683307710000},"page":"1-5","source":"Crossref","is-referenced-by-count":1,"title":["Towards a More Stable and General Subgraph Information Bottleneck"],"prefix":"10.1109","author":[{"given":"Hongzhi","family":"Liu","sequence":"first","affiliation":[{"name":"Xi’an Jiaotong University"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Kaizhong","family":"Zheng","sequence":"additional","affiliation":[{"name":"Xi’an Jiaotong University"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Shujian","family":"Yu","sequence":"additional","affiliation":[{"name":"Vrije Universiteit Amsterdam,Department of Computer Science"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Badong","family":"Chen","sequence":"additional","affiliation":[{"name":"Xi’an Jiaotong University"}],"role":[{"role":"author","vocabulary":"crossref"}]}],"member":"263","reference":[{"key":"ref1","article-title":"Brainib: Interpretable brain network-based psychiatric diagnosis with graph information bottleneck","author":"Zheng","year":"2022"},{"key":"ref2","doi-asserted-by":"publisher","DOI":"10.26599\/BDMA.2021.9020010"},{"key":"ref3","article-title":"N-gram graph: Simple unsupervised representation for graphs, with applications to molecules","volume":"32","author":"Liu","year":"2019","journal-title":"NeurIPS"},{"key":"ref4","doi-asserted-by":"publisher","DOI":"10.1109\/tits.2019.2935152"},{"key":"ref5","first-page":"26","article-title":"Please stop explaining black box models for high stakes decisions","volume":"1050","author":"Rudin","year":"2018","journal-title":"Stat"},{"key":"ref6","doi-asserted-by":"publisher","DOI":"10.1109\/TPAMI.2022.3204236"},{"key":"ref7","article-title":"Gnnexplainer: Generating explanations for graph neural networks","volume":"32","author":"Ying","year":"2019","journal-title":"NeurIPS"},{"key":"ref8","first-page":"19620","article-title":"Parameterized explainer for graph neural network","volume":"33","author":"Luo","year":"2020","journal-title":"NeurIPS"},{"key":"ref9","article-title":"Discovering invariant rationales for graph neural networks","author":"Wu","year":"2022"},{"key":"ref10","article-title":"The information bottleneck method","author":"Tishby","year":"2000"},{"key":"ref11","first-page":"20437","article-title":"Graph information bottleneck","volume":"33","author":"Wu","year":"2020","journal-title":"NeurIPS"},{"key":"ref12","doi-asserted-by":"publisher","DOI":"10.1109\/tpami.2021.3112205"},{"key":"ref13","doi-asserted-by":"publisher","DOI":"10.1109\/tpami.2019.2932976"},{"key":"ref14","article-title":"Mine: mutual information neural estimation","author":"Belghazi","year":"2018","journal-title":"ICML"},{"key":"ref15","doi-asserted-by":"publisher","DOI":"10.1016\/j.aiopen.2021.01.001"},{"key":"ref16","article-title":"How powerful are graph neural networks","author":"Xu","year":"2018","journal-title":"CVPR"},{"key":"ref17","article-title":"Graph attention networks","author":"Velikovi\u0107","year":"2017","journal-title":"ICLR"},{"key":"ref18","article-title":"Inductive representation learning on large graphs","volume":"30","author":"Hamilton","year":"2017","journal-title":"NeurIPS"},{"key":"ref19","article-title":"Order matters: Sequence to sequence for sets","author":"Vinyals","year":"2015","journal-title":"ICLR"},{"key":"ref20","doi-asserted-by":"publisher","DOI":"10.1609\/aaai.v32i1.11782"},{"key":"ref21","article-title":"Hierarchical graph representation learning with differentiable pooling","volume":"31","author":"Ying","year":"2018","journal-title":"NeurIPS"},{"key":"ref22","doi-asserted-by":"publisher","DOI":"10.1145\/3292500.3330982"},{"key":"ref23","first-page":"3734","article-title":"Self-attention graph pooling","volume-title":"ICML.","author":"Lee","year":"2019"},{"key":"ref24","first-page":"15524","article-title":"Interpretable and generalizable graph learning via stochastic attention mechanism","volume-title":"ICML.","author":"Miao","year":"2022"},{"key":"ref25","article-title":"Semi-supervised classification with graph convolutional networks","author":"Kipf","year":"2016","journal-title":"ICLR"},{"key":"ref26","doi-asserted-by":"publisher","DOI":"10.1109\/tpami.2020.2999032"},{"key":"ref27","doi-asserted-by":"publisher","DOI":"10.1109\/CVPR52688.2022.01879"},{"key":"ref28","first-page":"6827","article-title":"What makes for good views for contrastive learning?","volume":"33","author":"Tian","year":"2020","journal-title":"NeurIPS"},{"key":"ref29","article-title":"Categorical reparameterization with gumbel-softmax","author":"Jang","year":"2016","journal-title":"ICLR"},{"key":"ref30","doi-asserted-by":"publisher","DOI":"10.1609\/aaai.v36i8.20898"},{"key":"ref31","doi-asserted-by":"publisher","DOI":"10.1021\/jm00106a046"},{"key":"ref32","doi-asserted-by":"publisher","DOI":"10.1093\/bioinformatics\/bti1007"},{"key":"ref33","doi-asserted-by":"publisher","DOI":"10.1007\/s10115-007-0103-5"},{"key":"ref34","article-title":"A simple yet effective baseline for nonattributed graph classification","volume-title":"ICLR Workshop: Representation Learning on Graphs and Manifolds","author":"Cai"},{"key":"ref35","doi-asserted-by":"publisher","DOI":"10.3389\/fpsyt.2021.736755"},{"key":"ref36","doi-asserted-by":"publisher","DOI":"10.1097\/WNR.0000000000001300"}],"event":{"name":"ICASSP 2023 - 2023 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP)","location":"Rhodes Island, Greece","start":{"date-parts":[[2023,6,4]]},"end":{"date-parts":[[2023,6,10]]}},"container-title":["ICASSP 2023 - 2023 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP)"],"original-title":[],"link":[{"URL":"http:\/\/xplorestaging.ieee.org\/ielx7\/10094559\/10094560\/10094812.pdf?arnumber=10094812","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2024,2,29]],"date-time":"2024-02-29T22:33:52Z","timestamp":1709246032000},"score":1,"resource":{"primary":{"URL":"https:\/\/ieeexplore.ieee.org\/document\/10094812\/"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2023,6,4]]},"references-count":36,"URL":"https:\/\/doi.org\/10.1109\/icassp49357.2023.10094812","relation":{},"subject":[],"published":{"date-parts":[[2023,6,4]]}}}