{"type":"ScholarlyArticle","title":"Gapformer: Graph Transformer with Graph Pooling for Node Classification","authors":[{"name":"Chuang Liu","slug":"chuang-liu","given":"Chuang","family":"Liu","affiliation":"School of Computer Science , Wuhan University , Wuhan , China"},{"name":"Yibing Zhan","slug":"yibing-zhan","given":"Yibing","family":"Zhan","affiliation":"JD Explore Academy , JD .com , China"},{"name":"Xueqi Ma","slug":"xueqi-ma","given":"Xueqi","family":"Ma","affiliation":"School of Computing and Information Systems , The University of Melbourne , Melbourne , Australia"},{"name":"Liang Ding","slug":"liang-ding","given":"Liang","family":"Ding","affiliation":"JD Explore Academy , JD .com , China"},{"name":"Dapeng Tao","slug":"dapeng-tao","given":"Dapeng","family":"Tao","affiliation":"School of Computer Science , Yunnan University , Kunming , China; Yunnan Key Laboratory of Media Convergence , Kunming , China"},{"name":"Jia Wu","slug":"jia-wu","given":"Jia","family":"Wu","affiliation":"School of Computing , Macquarie University , Sydney , Australia"},{"name":"Wenbin Hu","slug":"wenbin-hu","given":"Wenbin","family":"Hu","affiliation":"School of Computer Science , Wuhan University , Wuhan , China"}],"abstract":"Graph Transformers (GTs) have proved their advantage in graph-level tasks. However, existing GTs still perform unsatisfactorily on the node classification task due to 1) the overwhelming unrelated information obtained from a vast number of irrelevant distant nodes and 2) the quadratic complexity regarding the number of nodes via the fully connected attention mechanism. In this paper, we present Gapformer, a method for node classification that deeply incorporates Graph Transformer with Graph Pooling. More specifically, Gapformer coarsens the large-scale nodes of a graph into a smaller number of pooling nodes via local or global graph pooling methods, and then computes the attention solely with the pooling nodes rather than all other nodes. In such a manner, the negative influence of the overwhelming unrelated nodes is mitigated while maintaining the long-range information, and the quadratic complexity is reduced to linear complexity with respect to the fixed number of pooling nodes. Extensive experiments on 13 node classification datasets, including homophilic and heterophilic graph datasets, demonstrate the competitive performance of Gapformer over existing Graph Neural Networks and GTs.","doi":"10.24963/ijcai.2023/244","url":"https://ijcai.org/proceedings/2023/244","pdf":"https://ijcai.org/proceedings/2023/0244.pdf","markdown":"https://ijcai.org/proceedings/2023/244.md","bibtex":"https://ijcai.org/proceedings/2023/bibtex/244","volume":{"slug":"2023","title":"Proceedings of the Thirty-Second International Joint Conference on Artificial Intelligence","short_title":"IJCAI 2023","url":"https://ijcai.org/proceedings/2023/","year":2023,"location":"Macao, SAR","dates":"19-25 August 2023","editors":"Edith Elkind","isbn":"978-1-956792-03-4","publisher":"International Joint Conferences on Artificial Intelligence Organization","doi_prefix":"10.24963/ijcai.2023"},"section":"Data Mining","track":"Main Track","number":244,"pages":{"first":2196,"last":2205},"keywords":["Data Mining: DM: Networks","Data Mining: DM: Mining graphs"],"videos":[],"references":[{"position":1,"raw":"Cornell Texas Wisconsin Actor GCN-based methods GCN [Kipf and Welling, 2017] 45.67 ±7.96 60.81 ±8.03 52.55 ±4.27 28.73 ±1.17 GatedGCN [Li et al., 2016] 72.70 ±5.33 75.40 ±4.26 81.37 ±3.31 35.13 ±1.10 APPNP [Gasteiger et al., 2019] 41.35 ±7.15 61.62 ±5.37 55.29 ±3.90 29.42 ±0.81 GCNII [Chen et al., 2020] 44.32 ±5.81 58.91 ±4.32 52.54 ±7.32 25.40 ±0.97 GAT [Veličković et al., 2018] 47.02 ±7.66 62.16 ±4.52 57.45 ±3.51 28.33 ±1.13 GATv2 [Brody et al., 2022] 50.27 ±8.97 60.54 ±4.55 52.74 ±3.96 28.79 ±1.47 SuperGAT [Kim and Oh, 2021] 43.51 ±6.55 59.99 ±4.64 53.52 ±4.64 28.08 ±1.03 Heterophily-based methods MLP [LeCun et al., 2015] 71.62 ±5.57 77.83 ±5.24 82.15 ±6.93 33.26 ±0.91 MixHop [Sami et al., 2019] 76.48 ±2.97 83.24 ±4.48 85.48 ±3.06 34.92 ±0.91 H2GCN [Zhu et al., 2020] 75.40 ±4.09 79.73 ±3.25 77.57 ±4.11 36.18 ±0.45 FAGCN [Bo et al., 2021] 67.56 ±5.26 75.67 ±4.68 75.29 ±3.06 32.13 ±1.33 GPRGNN [Chien et al., 2021] 76.76 ±2.16 81.08 ±4.35 82.66 ±5.62 35.30 ±0.80 Graph Transformer-based methods SAN [Kreuzer et al., 2021] 50.85 ±8.54 60.17 ±6.66 51.37 ±3.08 27.12 ±2.59 UniMP [Shi et al., 2021] 66.48 ±12.5 73.51 ±8.44 79.60 ±5.41 35.15 ±0.84 DET [Guo et al., 2022] 41.35 ±7.45 56.76 ±4.98 54.90 ±6.56 28.94 ±0.64 NAGphormer [Chen et al., 2023] 56.22 ±8.08 63.51 ±6.53 62.55 ±6.22 34.33 ±0.94 Gapformer (w/o GP) 61.89 ±5.85 70.54 ±4.75 75.29 ±5.12 33.86 ±0.79","title":"Cornell Texas Wisconsin Actor GCN-based methods GCN","authors":"Li","year":"2015","venue":"Kipf and Welling","doi":null,"url":null,"internal_url":null},{"position":2,"raw":"Gapformer (AGP-G) 77.57 ±3.43 80.27 ±4.01 83.53 ±3.42 36.90 ±0.82 Gapformer (AGP-L) 76.22 ±2.65 79.73 ±5.16 82.15 ±2.22 36.47 ±1.02 Notations: 1) Gapformer (w/o GP) refers to Gapformer without graph pooling, which is also the GT-sparse baseline model [","title":"Gapformer","authors":null,"year":null,"venue":"Gapformer","doi":null,"url":null,"internal_url":null},{"position":3,"raw":"Dwivedi and Bresson, 2021]. 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