{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2025,11,15]],"date-time":"2025-11-15T10:29:35Z","timestamp":1763202575617,"version":"3.28.0"},"reference-count":26,"publisher":"IEEE","license":[{"start":{"date-parts":[[2021,10,25]],"date-time":"2021-10-25T00:00:00Z","timestamp":1635120000000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/ieeexplore.ieee.org\/Xplorehelp\/downloads\/license-information\/IEEE.html"},{"start":{"date-parts":[[2021,10,25]],"date-time":"2021-10-25T00:00:00Z","timestamp":1635120000000},"content-version":"stm-asf","delay-in-days":0,"URL":"https:\/\/doi.org\/10.15223\/policy-029"},{"start":{"date-parts":[[2021,10,25]],"date-time":"2021-10-25T00:00:00Z","timestamp":1635120000000},"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":[[2021,10,25]]},"DOI":"10.1109\/mlsp52302.2021.9596069","type":"proceedings-article","created":{"date-parts":[[2021,11,15]],"date-time":"2021-11-15T17:53:58Z","timestamp":1636998838000},"page":"1-6","source":"Crossref","is-referenced-by-count":6,"title":["Hierarchical Graph Neural Nets can Capture Long-Range Interactions"],"prefix":"10.1109","author":[{"given":"Ladislav","family":"Rampasek","sequence":"first","affiliation":[]},{"given":"Guy","family":"Wolf","sequence":"additional","affiliation":[]}],"member":"263","reference":[{"key":"ref10","article-title":"Multipole graph neural operator for parametric partial differential equations","author":"li","year":"2020","journal-title":"Adv In NeurIPS 33"},{"key":"ref11","article-title":"Hierarchical graph representation learning with differentiable pooling","volume":"31","author":"ying","year":"2018","journal-title":"Adv in NeurIPS"},{"key":"ref12","article-title":"Edge contraction pooling for graph neural networks","author":"diehl","year":"2019","journal-title":"ArXiv"},{"key":"ref13","first-page":"3734","article-title":"Self-attention graph pooling","author":"lee","year":"2019","journal-title":"Proc of ICML PMLR"},{"key":"ref14","doi-asserted-by":"publisher","DOI":"10.1088\/1742-5468\/2008\/10\/P10008"},{"key":"ref15","article-title":"Semi-supervised classification with graph convolutional networks","author":"kipf","year":"2017","journal-title":"ICLRE"},{"key":"ref16","doi-asserted-by":"crossref","DOI":"10.1007\/978-3-319-93417-4_38","article-title":"Modeling relational data with graph convolutional networks","author":"schlichtkrull","year":"2018","journal-title":"European Semantic Web Conference"},{"key":"ref17","article-title":"Graph attention networks","author":"velickovic","year":"2018","journal-title":"ICLRE"},{"key":"ref18","article-title":"ChebNet: Efficient and stable constructions of deep neural networks with rectified power units using chebyshev approximations","author":"tang","year":"2019","journal-title":"ArXiv"},{"key":"ref19","article-title":"A fair comparison of graph neural networks for graph classification","author":"errica","year":"2020","journal-title":"ICLRE"},{"key":"ref4","article-title":"How powerful are graph neural networks?","author":"xu","year":"2019","journal-title":"ICLRE"},{"key":"ref3","first-page":"1263","article-title":"Neural message passing for quantum chemistry","author":"gilmer","year":"2017","journal-title":"Proc of ICML"},{"key":"ref6","article-title":"Deeper insights into graph convolutional networks for semi -supervised learning","author":"li","year":"2018","journal-title":"AAAI Conference on Artificial Intelligence"},{"key":"ref5","article-title":"On the bottleneck of graph neural networks and its practical implications","author":"alon","year":"2020","journal-title":"ICLRE"},{"key":"ref8","first-page":"2083","article-title":"Graph U-nets","author":"gao","year":"2019","journal-title":"Proc of ICML"},{"key":"ref7","first-page":"14498","article-title":"Scattering GCN: Overcoming oversmoothness in graph convolutional networks","author":"min","year":"2020","journal-title":"Adv In NeurIPS 33"},{"key":"ref2","article-title":"Geometric deep learning: Grids, groups, graphs, geodesics, and gauges","author":"bronstein","year":"2021","journal-title":"ArXiv"},{"key":"ref9","article-title":"Graph cross networks with vertex infomax pooling","volume":"33","author":"li","year":"2020","journal-title":"Adv in NeurIPS"},{"key":"ref1","doi-asserted-by":"publisher","DOI":"10.2200\/S01045ED1V01Y202009AIM046"},{"key":"ref20","first-page":"40","article-title":"Revisiting semi-supervised learning with graph embeddings","volume":"48","author":"yang","year":"2016","journal-title":"Proc of ICML"},{"key":"ref22","article-title":"Combining label propagation and simple models outperforms graph neural networks","author":"huang","year":"2021","journal-title":"ICLRE"},{"key":"ref21","article-title":"Beyond homophily in graph neural networks: Current limitations and effective designs","volume":"33","author":"zhu","year":"2020","journal-title":"Adv in NeurIPS"},{"key":"ref24","article-title":"Open graph benchmark: Datasets for machine learning on graphs","author":"hu","year":"2020","journal-title":"Adv In NeurIPS 33"},{"key":"ref23","article-title":"TUDataset: A collection of benchmark datasets for learning with graphs","author":"morris","year":"2020","journal-title":"ICML 2020 GRL+ Workshop"},{"key":"ref26","doi-asserted-by":"crossref","DOI":"10.1609\/aaai.v29i1.9277","article-title":"The network data repository with interactive graph analytics and visualization","author":"rossi","year":"2015","journal-title":"Proc Of AAAI"},{"key":"ref25","article-title":"Graph networks with spectral message passing","author":"stachenfeld","year":"2021","journal-title":"ArXiv"}],"event":{"name":"2021 IEEE 31st International Workshop on Machine Learning for Signal Processing (MLSP)","start":{"date-parts":[[2021,10,25]]},"location":"Gold Coast, Australia","end":{"date-parts":[[2021,10,28]]}},"container-title":["2021 IEEE 31st International Workshop on Machine Learning for Signal Processing (MLSP)"],"original-title":[],"link":[{"URL":"http:\/\/xplorestaging.ieee.org\/ielx7\/9596063\/9596068\/09596069.pdf?arnumber=9596069","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2023,11,12]],"date-time":"2023-11-12T12:37:29Z","timestamp":1699792649000},"score":1,"resource":{"primary":{"URL":"https:\/\/ieeexplore.ieee.org\/document\/9596069\/"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2021,10,25]]},"references-count":26,"URL":"https:\/\/doi.org\/10.1109\/mlsp52302.2021.9596069","relation":{},"subject":[],"published":{"date-parts":[[2021,10,25]]}}}