{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,6,22]],"date-time":"2026-06-22T17:54:14Z","timestamp":1782150854544,"version":"3.54.5"},"reference-count":55,"publisher":"Elsevier BV","license":[{"start":{"date-parts":[[2026,12,1]],"date-time":"2026-12-01T00:00:00Z","timestamp":1796083200000},"content-version":"tdm","delay-in-days":0,"URL":"https:\/\/www.elsevier.com\/tdm\/userlicense\/1.0\/"},{"start":{"date-parts":[[2026,12,1]],"date-time":"2026-12-01T00:00:00Z","timestamp":1796083200000},"content-version":"tdm","delay-in-days":0,"URL":"https:\/\/www.elsevier.com\/legal\/tdmrep-license"},{"start":{"date-parts":[[2026,12,1]],"date-time":"2026-12-01T00:00:00Z","timestamp":1796083200000},"content-version":"stm-asf","delay-in-days":0,"URL":"https:\/\/doi.org\/10.15223\/policy-017"},{"start":{"date-parts":[[2026,12,1]],"date-time":"2026-12-01T00:00:00Z","timestamp":1796083200000},"content-version":"stm-asf","delay-in-days":0,"URL":"https:\/\/doi.org\/10.15223\/policy-037"},{"start":{"date-parts":[[2026,12,1]],"date-time":"2026-12-01T00:00:00Z","timestamp":1796083200000},"content-version":"stm-asf","delay-in-days":0,"URL":"https:\/\/doi.org\/10.15223\/policy-012"},{"start":{"date-parts":[[2026,12,1]],"date-time":"2026-12-01T00:00:00Z","timestamp":1796083200000},"content-version":"stm-asf","delay-in-days":0,"URL":"https:\/\/doi.org\/10.15223\/policy-029"},{"start":{"date-parts":[[2026,12,1]],"date-time":"2026-12-01T00:00:00Z","timestamp":1796083200000},"content-version":"stm-asf","delay-in-days":0,"URL":"https:\/\/doi.org\/10.15223\/policy-004"}],"funder":[{"DOI":"10.13039\/100012542","name":"Sichuan Provincial Science and Technology Support Program","doi-asserted-by":"publisher","award":["2024ZDZX0011"],"award-info":[{"award-number":["2024ZDZX0011"]}],"id":[{"id":"10.13039\/100012542","id-type":"DOI","asserted-by":"publisher"}]},{"DOI":"10.13039\/100012542","name":"Sichuan Provincial Science and Technology Support Program","doi-asserted-by":"publisher","award":["2023ZYD0165"],"award-info":[{"award-number":["2023ZYD0165"]}],"id":[{"id":"10.13039\/100012542","id-type":"DOI","asserted-by":"publisher"}]}],"content-domain":{"domain":["elsevier.com","sciencedirect.com"],"crossmark-restriction":true},"short-container-title":["Pattern Recognition"],"published-print":{"date-parts":[[2026,12]]},"DOI":"10.1016\/j.patcog.2026.114235","type":"journal-article","created":{"date-parts":[[2026,6,13]],"date-time":"2026-06-13T00:55:54Z","timestamp":1781312154000},"page":"114235","update-policy":"https:\/\/doi.org\/10.1016\/elsevier_cm_policy","source":"Crossref","is-referenced-by-count":0,"special_numbering":"PC","title":["ALS: Attentive long-short-range message passing for graph representation learning"],"prefix":"10.1016","volume":"180","author":[{"ORCID":"https:\/\/orcid.org\/0000-0002-8494-218X","authenticated-orcid":false,"given":"Yi","family":"Luo","sequence":"first","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Xu","family":"Sun","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Guangchun","family":"Luo","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-3712-2349","authenticated-orcid":false,"given":"Aiguo","family":"Chen","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]}],"member":"78","reference":[{"key":"10.1016\/j.patcog.2026.114235_b1","series-title":"ICLR (Poster)","article-title":"Graph attention networks","author":"Velickovic","year":"2018"},{"key":"10.1016\/j.patcog.2026.114235_b2","series-title":"ICLR (Poster)","article-title":"Semi-supervised classification with graph convolutional networks","author":"Kipf","year":"2017"},{"key":"10.1016\/j.patcog.2026.114235_b3","doi-asserted-by":"crossref","first-page":"22326","DOI":"10.52202\/068431-1622","article-title":"Long range graph benchmark","volume":"35","author":"Dwivedi","year":"2022","journal-title":"Adv. Neural Inf. Process. Syst."},{"key":"10.1016\/j.patcog.2026.114235_b4","doi-asserted-by":"crossref","DOI":"10.1016\/j.patcog.2023.110115","article-title":"UniG-encoder: A universal feature encoder for graph and hypergraph node classification","volume":"147","author":"Zou","year":"2024","journal-title":"Pattern Recognit."},{"key":"10.1016\/j.patcog.2026.114235_b5","series-title":"Proceedings of the ACM on Web Conference 2025","first-page":"2860","article-title":"LLGformer: Learnable long-range graph transformer for traffic flow prediction","author":"Jin","year":"2025"},{"key":"10.1016\/j.patcog.2026.114235_b6","article-title":"HopGAT: A multi-hop graph attention network with heterophily and degree awareness","volume":"172","author":"Zhang","year":"2026","journal-title":"Pattern Recognit."},{"key":"10.1016\/j.patcog.2026.114235_b7","series-title":"IJCAI","first-page":"3089","article-title":"Multi-hop attention graph neural networks","author":"Wang","year":"2021"},{"key":"10.1016\/j.patcog.2026.114235_b8","series-title":"ICASSP 2022-2022 IEEE International Conference on Acoustics, Speech and Signal Processing","first-page":"3578","article-title":"Personalized pagerank graph attention networks","author":"Choi","year":"2022"},{"issue":"3","key":"10.1016\/j.patcog.2026.114235_b9","doi-asserted-by":"crossref","first-page":"321","DOI":"10.1137\/140976649","article-title":"PageRank beyond the web","volume":"57","author":"Gleich","year":"2015","journal-title":"SIAM Rev."},{"key":"10.1016\/j.patcog.2026.114235_b10","series-title":"ICML","first-page":"1725","article-title":"Simple and deep graph convolutional networks","volume":"vol. 119","author":"Chen","year":"2020"},{"key":"10.1016\/j.patcog.2026.114235_b11","series-title":"ICLR (Poster)","article-title":"Predict then propagate: Graph neural networks meet personalized PageRank","author":"Klicpera","year":"2019"},{"key":"10.1016\/j.patcog.2026.114235_b12","series-title":"KDD","first-page":"2464","article-title":"Scaling graph neural networks with approximate PageRank","author":"Bojchevski","year":"2020"},{"key":"10.1016\/j.patcog.2026.114235_b13","series-title":"ICLR","article-title":"Adaptive universal generalized PageRank graph neural network","author":"Chien","year":"2021"},{"key":"10.1016\/j.patcog.2026.114235_b14","doi-asserted-by":"crossref","DOI":"10.1016\/j.patcog.2024.110738","article-title":"Heterophily-aware graph attention network","volume":"156","author":"Wang","year":"2024","journal-title":"Pattern Recognit."},{"key":"10.1016\/j.patcog.2026.114235_b15","series-title":"ICLR","article-title":"A critical look at the evaluation of GNNs under heterophily: Are we really making progress?","author":"Platonov","year":"2023"},{"key":"10.1016\/j.patcog.2026.114235_b16","doi-asserted-by":"crossref","first-page":"14501","DOI":"10.52202\/068431-1054","article-title":"Recipe for a general, powerful, scalable graph transformer","volume":"35","author":"Ramp\u00e1\u0161ek","year":"2022","journal-title":"Adv. Neural Inf. Process. Syst."},{"key":"10.1016\/j.patcog.2026.114235_b17","series-title":"Proceedings of the 30th ACM SIGKDD Conference on Knowledge Discovery and Data Mining","first-page":"119","article-title":"Graph mamba: Towards learning on graphs with state space models","author":"Behrouz","year":"2024"},{"key":"10.1016\/j.patcog.2026.114235_b18","series-title":"NIPS","first-page":"5998","article-title":"Attention is all you need","author":"Vaswani","year":"2017"},{"key":"10.1016\/j.patcog.2026.114235_b19","doi-asserted-by":"crossref","DOI":"10.1016\/j.cosrev.2026.100966","article-title":"How attention is applied to graph neural networks: A comprehensive survey","volume":"61","author":"He","year":"2026","journal-title":"Comput. Sci. Rev."},{"key":"10.1016\/j.patcog.2026.114235_b20","series-title":"ICML","article-title":"Cooperative graph neural networks","author":"Finkelshtein","year":"2024"},{"key":"10.1016\/j.patcog.2026.114235_b21","series-title":"Residual gated graph ConvNets","author":"Bresson","year":"2017"},{"key":"10.1016\/j.patcog.2026.114235_b22","series-title":"Forty-First International Conference on Machine Learning","article-title":"Less is more: on the over-globalizing problem in graph transformers","author":"Xing","year":"2024"},{"key":"10.1016\/j.patcog.2026.114235_b23","series-title":"Proceedings of the 32nd ACM SIGKDD Conference on Knowledge Discovery and Data Mining V.1","first-page":"2663","article-title":"Benchmarking positional encodings for GNNs and graph transformers","author":"Gr\u00f6tschla","year":"2026"},{"key":"10.1016\/j.patcog.2026.114235_b24","series-title":"International Conference on Machine Learning","first-page":"31613","article-title":"Exphormer: Sparse transformers for graphs","author":"Shirzad","year":"2023"},{"key":"10.1016\/j.patcog.2026.114235_b25","unstructured":"J. Chen, K. Gao, G. Li, K. He, NAGphormer: A Tokenized Graph Transformer for Node Classification in Large Graphs, in: International Conference on Learning Representations, 2022."},{"key":"10.1016\/j.patcog.2026.114235_b26","unstructured":"H. Shirzad, A. Velingker, B. Venkatachalam, D.J. Sutherland, A.K. Sinop, Exphormer: Sparse Transformers for Graphs, in: International Conference on Machine Learning, 2023."},{"key":"10.1016\/j.patcog.2026.114235_b27","series-title":"Proceedings of the Thirty-Fourth International Joint Conference on Artificial Intelligence","first-page":"6506","article-title":"Leveraging personalized PageRank and higher-order topological structures for heterophily mitigation in graph neural networks","author":"Wang","year":"2025"},{"key":"10.1016\/j.patcog.2026.114235_b28","article-title":"Deep equilibrium models","volume":"32","author":"Bai","year":"2019","journal-title":"Adv. Neural Inf. Process. Syst."},{"key":"10.1016\/j.patcog.2026.114235_b29","first-page":"11984","article-title":"Implicit graph neural networks","volume":"33","author":"Gu","year":"2020","journal-title":"Adv. Neural Inf. Process. Syst."},{"key":"10.1016\/j.patcog.2026.114235_b30","first-page":"18762","article-title":"Eignn: Efficient infinite-depth graph neural networks","volume":"34","author":"Liu","year":"2021","journal-title":"Adv. Neural Inf. Process. Syst."},{"key":"10.1016\/j.patcog.2026.114235_b31","doi-asserted-by":"crossref","DOI":"10.1016\/j.neunet.2025.108143","article-title":"Implicit graph neural networks with flexible propagation operators","volume":"194","author":"Pi","year":"2026","journal-title":"Neural Netw."},{"key":"10.1016\/j.patcog.2026.114235_b32","doi-asserted-by":"crossref","first-page":"397","DOI":"10.1016\/j.cam.2015.09.027","article-title":"FOM accelerated by an extrapolation method for solving PageRank problems","volume":"296","author":"Zhang","year":"2016","journal-title":"J. Comput. Appl. Math."},{"key":"10.1016\/j.patcog.2026.114235_b33","series-title":"Iterative Methods for Sparse Linear Systems","author":"Saad","year":"2003"},{"key":"10.1016\/j.patcog.2026.114235_b34","volume":"vol. 49","author":"Hestenes","year":"1952"},{"key":"10.1016\/j.patcog.2026.114235_b35","first-page":"7793","article-title":"Beyond homophily in graph neural networks: Current limitations and effective designs","volume":"33","author":"Zhu","year":"2020","journal-title":"Adv. Neural Inf. Process. Syst."},{"key":"10.1016\/j.patcog.2026.114235_b36","series-title":"Pitfalls of graph neural network evaluation","author":"Shchur","year":"2018"},{"key":"10.1016\/j.patcog.2026.114235_b37","series-title":"Wiki-CS: A wikipedia-based benchmark for graph neural networks","author":"Mernyei","year":"2020"},{"key":"10.1016\/j.patcog.2026.114235_b38","first-page":"22118","article-title":"Open graph benchmark: Datasets for machine learning on graphs","volume":"33","author":"Hu","year":"2020","journal-title":"Adv. Neural Inf. Process. Syst."},{"key":"10.1016\/j.patcog.2026.114235_b39","article-title":"Where did the gap go? Reassessing the long-range graph benchmark","volume":"2024","author":"T\u201donshoff","year":"2024","journal-title":"Trans. Mach. Learn. Res."},{"issue":"4","key":"10.1016\/j.patcog.2026.114235_b40","doi-asserted-by":"crossref","first-page":"73:1","DOI":"10.1145\/3490478","article-title":"Combining graph convolutional neural networks and label propagation","volume":"40","author":"Wang","year":"2022","journal-title":"ACM Trans. Inf. Syst."},{"key":"10.1016\/j.patcog.2026.114235_b41","series-title":"NIPS","first-page":"1024","article-title":"Inductive representation learning on large graphs","author":"Hamilton","year":"2017"},{"key":"10.1016\/j.patcog.2026.114235_b42","series-title":"ICDM","first-page":"1287","article-title":"Two sides of the same coin: Heterophily and oversmoothing in graph convolutional neural networks","author":"Yan","year":"2022"},{"key":"10.1016\/j.patcog.2026.114235_b43","series-title":"The Eleventh International Conference on Learning Representations","article-title":"Ordered GNN: ordering message passing to deal with heterophily and over-smoothing","author":"Song","year":"2023"},{"key":"10.1016\/j.patcog.2026.114235_b44","series-title":"NeurIPS","article-title":"High-order pooling for graph neural networks with tensor decomposition","author":"Hua","year":"2022"},{"key":"10.1016\/j.patcog.2026.114235_b45","series-title":"ICML","first-page":"13242","article-title":"Finding global homophily in graph neural networks when meeting heterophily","volume":"vol. 162","author":"Li","year":"2022"},{"key":"10.1016\/j.patcog.2026.114235_b46","doi-asserted-by":"crossref","DOI":"10.1016\/j.jocs.2022.101695","article-title":"Simplifying approach to node classification in Graph Neural Networks","volume":"62","author":"Maurya","year":"2022","journal-title":"J. Comput. Sci."},{"key":"10.1016\/j.patcog.2026.114235_b47","series-title":"ICLR","article-title":"Gradient gating for deep multi-rate learning on graphs","author":"Rusch","year":"2023"},{"key":"10.1016\/j.patcog.2026.114235_b48","series-title":"Neural Information Processing Systems","article-title":"Large scale learning on non-homophilous graphs: New benchmarks and strong simple methods","author":"Lim","year":"2021"},{"key":"10.1016\/j.patcog.2026.114235_b49","series-title":"ICLR","article-title":"Strategies for pre-training graph neural networks","author":"Hu","year":"2020"},{"key":"10.1016\/j.patcog.2026.114235_b50","series-title":"International Conference on Machine Learning","first-page":"38926","article-title":"LazyGNN: Large-scale graph neural networks via lazy propagation","volume":"vol. 202","author":"Xue","year":"2023"},{"key":"10.1016\/j.patcog.2026.114235_b51","series-title":"NeurIPS","first-page":"8024","article-title":"PyTorch: An imperative style, high-performance deep learning library","author":"Paszke","year":"2019"},{"key":"10.1016\/j.patcog.2026.114235_b52","doi-asserted-by":"crossref","first-page":"4423","DOI":"10.1109\/TNNLS.2021.3129649","article-title":"Learning aligned vertex convolutional networks for graph classification","volume":"35","author":"Cui","year":"2021","journal-title":"IEEE Trans. Neural Netw. Learn. Syst."},{"key":"10.1016\/j.patcog.2026.114235_b53","doi-asserted-by":"crossref","unstructured":"A. Feng, C. You, S. Wang, L. Tassiulas, KerGNNs: Interpretable Graph Neural Networks with Graph Kernels, in: AAAI Conference on Artificial Intelligence, 2022.","DOI":"10.1609\/aaai.v36i6.20615"},{"key":"10.1016\/j.patcog.2026.114235_b54","doi-asserted-by":"crossref","DOI":"10.1016\/j.patcog.2022.108628","article-title":"Personalized knowledge-aware recommendation with collaborative and attentive graph convolutional networks","volume":"128","author":"Dai","year":"2022","journal-title":"Pattern Recognit."},{"key":"10.1016\/j.patcog.2026.114235_b55","series-title":"ICLR","article-title":"Polynormer: Polynomial-expressive graph transformer in linear time","author":"Deng","year":"2024"}],"container-title":["Pattern Recognition"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/api.elsevier.com\/content\/article\/PII:S0031320326012008?httpAccept=text\/xml","content-type":"text\/xml","content-version":"vor","intended-application":"text-mining"},{"URL":"https:\/\/api.elsevier.com\/content\/article\/PII:S0031320326012008?httpAccept=text\/plain","content-type":"text\/plain","content-version":"vor","intended-application":"text-mining"}],"deposited":{"date-parts":[[2026,6,22]],"date-time":"2026-06-22T17:44:15Z","timestamp":1782150255000},"score":1,"resource":{"primary":{"URL":"https:\/\/linkinghub.elsevier.com\/retrieve\/pii\/S0031320326012008"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2026,12]]},"references-count":55,"alternative-id":["S0031320326012008"],"URL":"https:\/\/doi.org\/10.1016\/j.patcog.2026.114235","relation":{},"ISSN":["0031-3203"],"issn-type":[{"value":"0031-3203","type":"print"}],"subject":[],"published":{"date-parts":[[2026,12]]},"assertion":[{"value":"Elsevier","name":"publisher","label":"This article is maintained by"},{"value":"ALS: Attentive long-short-range message passing for graph representation learning","name":"articletitle","label":"Article Title"},{"value":"Pattern Recognition","name":"journaltitle","label":"Journal Title"},{"value":"https:\/\/doi.org\/10.1016\/j.patcog.2026.114235","name":"articlelink","label":"CrossRef DOI link to publisher maintained version"},{"value":"article","name":"content_type","label":"Content Type"},{"value":"\u00a9 2026 Published by Elsevier Ltd.","name":"copyright","label":"Copyright"}],"article-number":"114235"}}