{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2025,4,17]],"date-time":"2025-04-17T08:49:00Z","timestamp":1744879740938,"version":"3.28.0"},"reference-count":48,"publisher":"IEEE","license":[{"start":{"date-parts":[[2024,6,30]],"date-time":"2024-06-30T00:00:00Z","timestamp":1719705600000},"content-version":"stm-asf","delay-in-days":0,"URL":"https:\/\/doi.org\/10.15223\/policy-029"},{"start":{"date-parts":[[2024,6,30]],"date-time":"2024-06-30T00:00:00Z","timestamp":1719705600000},"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":[[2024,6,30]]},"DOI":"10.1109\/ijcnn60899.2024.10651554","type":"proceedings-article","created":{"date-parts":[[2024,9,9]],"date-time":"2024-09-09T17:35:05Z","timestamp":1725903305000},"page":"1-9","source":"Crossref","is-referenced-by-count":1,"title":["Hybrid Focal and Full-Range Attention Based Graph Transformers"],"prefix":"10.1109","author":[{"given":"Minhong","family":"Zhu","sequence":"first","affiliation":[{"name":"Soochow University,School of Biology and Basic Medical Science,Suzhou,China"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Zhenhao","family":"Zhao","sequence":"additional","affiliation":[{"name":"Soochow University,School of Computer Science and Technology,Suzhou,China"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Weiran","family":"Cai","sequence":"additional","affiliation":[{"name":"Soochow University,School of Computer Science and Technology,Suzhou,China"}],"role":[{"role":"author","vocabulary":"crossref"}]}],"member":"263","reference":[{"key":"ref1","first-page":"1263","article-title":"Neural message passing for quantum chemistry","volume-title":"International Conference on Machine Learning","author":"Gilmer"},{"article-title":"Semi-supervised classification with graph convolutional networks","year":"2016","author":"Kipf","key":"ref2"},{"article-title":"How powerful are graph neural networks?","year":"2018","author":"Xu","key":"ref3"},{"article-title":"Graph attention networks","year":"2017","author":"Veli\u010dkovi\u0107","key":"ref4"},{"key":"ref5","doi-asserted-by":"publisher","DOI":"10.1609\/aaai.v32i1.11604"},{"article-title":"On the bottleneck of graph neural networks and its practical implications","year":"2020","author":"Alon","key":"ref6"},{"article-title":"Understanding over-squashing and bottlenecks on graphs via curvature","year":"2021","author":"Topping","key":"ref7"},{"key":"ref8","doi-asserted-by":"publisher","DOI":"10.48550\/ARXIV.1706.03762"},{"article-title":"An image is worth 16x16 words: Transformers for image recognition at scale","year":"2020","author":"Dosovitskiy","key":"ref9"},{"key":"ref10","first-page":"14501","article-title":"Recipe for a general, powerful, scalable graph transformer","volume":"35","author":"Ramp\u00e1\u0161ek","year":"2022","journal-title":"Advances in Neural Information Processing Systems"},{"article-title":"Attending to graph transformers","year":"2023","author":"M\u00fcller","key":"ref11"},{"key":"ref12","first-page":"21618","article-title":"Rethinking graph transformers with spectral attention","volume":"34","author":"Kreuzer","year":"2021","journal-title":"Advances in Neural Information Processing Systems"},{"article-title":"A generalization of transformer networks to graphs","year":"2020","author":"Dwivedi","key":"ref13"},{"key":"ref14","first-page":"28877","article-title":"Do transformers really perform badly for graph representation?","volume":"34","author":"Ying","year":"2021","journal-title":"Advances in Neural Information Processing Systems"},{"article-title":"Grpe: Relative positional encoding for graph transformer","year":"2022","author":"Park","key":"ref15"},{"key":"ref16","doi-asserted-by":"publisher","DOI":"10.1163\/9789004337862__com_070768"},{"article-title":"AutoGT: Automated graph transformer architecture search","volume-title":"The Eleventh International Conference on Learning Representations","author":"Zhang","key":"ref17"},{"key":"ref18","doi-asserted-by":"publisher","DOI":"10.24963\/ijcai.2023\/396"},{"article-title":"Graph inductive biases in transformers without message passing","year":"2023","author":"Ma","key":"ref19"},{"article-title":"Transformer for graphs: An overview from architecture perspective","year":"2022","author":"Min","key":"ref20"},{"key":"ref21","first-page":"13266","article-title":"Representing long-range context for graph neural networks with global attention","volume":"34","author":"Wu","year":"2021","journal-title":"Advances in Neural Information Processing Systems"},{"key":"ref22","first-page":"12559","article-title":"Self-supervised graph transformer on large-scale molecular data","volume":"33","author":"Rong","year":"2020","journal-title":"Advances in Neural Information Processing Systems"},{"key":"ref23","doi-asserted-by":"publisher","DOI":"10.1109\/ICCV48922.2021.01270"},{"article-title":"Graph-Bert: Only attention is needed for learning graph representations","year":"2020","author":"Zhang","key":"ref24"},{"key":"ref25","doi-asserted-by":"publisher","DOI":"10.1016\/j.neunet.2020.07.008"},{"key":"ref26","first-page":"22326","article-title":"Long range graph benchmark","volume":"35","author":"Prakash Dwivedi","year":"2022","journal-title":"Advances in Neural Information Processing Systems"},{"article-title":"Benchmarking graph neural networks","year":"2020","author":"Prakash Dwivedi","key":"ref27"},{"key":"ref28","article-title":"Self-attention in colors: Another take on encoding graph structure in transformers","author":"Menegaux","year":"2023","journal-title":"T Mach Learn Res"},{"key":"ref29","doi-asserted-by":"publisher","DOI":"10.1145\/3534678.3539296"},{"key":"ref30","first-page":"14582","article-title":"Pure transformers are powerful graph learners","volume":"35","author":"Kim","year":"2022","journal-title":"Advances in Neural Information Processing Systems"},{"key":"ref31","first-page":"8017","article-title":"Subgraph neural networks","volume":"33","author":"Alsentzer","year":"2020","journal-title":"Advances in Neural Information Processing Systems"},{"key":"ref32","first-page":"31376","article-title":"Understanding and extending subgraph gnns by rethinking their symmetries","volume":"35","author":"Frasca","year":"2022","journal-title":"Advances in Neural Information Processing Systems"},{"key":"ref33","first-page":"4776","article-title":"How powerful are k-hop message passing graph neural networks","volume":"35","author":"Feng","year":"2022","journal-title":"Advances in Neural Information Processing Systems"},{"key":"ref34","first-page":"12724","article-title":"A generalization of vit\/mlp-mixer to graphs","volume-title":"International Conference on Machine Learning","author":"He"},{"article-title":"NAGphormer: A tokenized graph transformer for node classification in large graphs","volume-title":"The Eleventh International Conference on Learning Representations","author":"Chen","key":"ref35"},{"article-title":"Exphormer: Sparse transformers for graphs","year":"2023","author":"Shirzad","key":"ref36"},{"key":"ref37","doi-asserted-by":"publisher","DOI":"10.1063\/5.0152833"},{"key":"ref38","doi-asserted-by":"publisher","DOI":"10.1145\/3580305.3599390"},{"article-title":"Gophormer: Ego-graph transformer for node classification","year":"2021","author":"Zhao","key":"ref39"},{"key":"ref40","first-page":"4301","article-title":"Your transformer may not be as powerful as you expect","volume":"35","author":"Luo","year":"2022","journal-title":"Advances in Neural Information Processing Systems"},{"article-title":"Masked transformer for neighhourhood-aware click-through rate prediction","year":"2022","author":"Min","key":"ref41"},{"article-title":"Bert: Pre-training of deep bidirectional transformers for language understanding","year":"2018","author":"Devlin","key":"ref42"},{"key":"ref43","first-page":"22118","article-title":"Open graph benchmark: Datasets for machine learning on graphs","volume":"33","author":"Hu","year":"2020","journal-title":"Advances in Neural Information Processing Systems"},{"article-title":"Residual gated graph convnets","year":"2017","author":"Bresson","key":"ref44"},{"key":"ref45","doi-asserted-by":"publisher","DOI":"10.1609\/aimag.v29i3.2157"},{"key":"ref46","doi-asserted-by":"publisher","DOI":"10.1093\/comnet\/cnab014"},{"key":"ref47","doi-asserted-by":"publisher","DOI":"10.24963\/ijcai.2021\/214"},{"key":"ref48","doi-asserted-by":"publisher","DOI":"10.1109\/TNN.2008.2005605"}],"event":{"name":"2024 International Joint Conference on Neural Networks (IJCNN)","start":{"date-parts":[[2024,6,30]]},"location":"Yokohama, Japan","end":{"date-parts":[[2024,7,5]]}},"container-title":["2024 International Joint Conference on Neural Networks (IJCNN)"],"original-title":[],"link":[{"URL":"http:\/\/xplorestaging.ieee.org\/ielx8\/10649807\/10649898\/10651554.pdf?arnumber=10651554","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2024,9,11]],"date-time":"2024-09-11T05:13:28Z","timestamp":1726031608000},"score":1,"resource":{"primary":{"URL":"https:\/\/ieeexplore.ieee.org\/document\/10651554\/"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2024,6,30]]},"references-count":48,"URL":"https:\/\/doi.org\/10.1109\/ijcnn60899.2024.10651554","relation":{},"subject":[],"published":{"date-parts":[[2024,6,30]]}}}