Poster "graph transformers" Papers
17 papers found
Conference
DUALFormer: Dual Graph Transformer
Zhuo Jiaming, Yuwei Liu, Yintong Lu et al.
GMV: A Unified and Efficient Graph Multi-View Learning Framework
Qipeng zhu, Jie Chen, Jian Pu et al.
GrokFormer: Graph Fourier Kolmogorov-Arnold Transformers
GUOGUO AI, Guansong Pang, Hezhe Qiao et al.
Learning Long Range Dependencies on Graphs via Random Walks
Dexiong Chen, Till Schulz, Karsten Borgwardt
Relieving the Over-Aggregating Effect in Graph Transformers
Junshu Sun, Wanxing Chang, Chenxue Yang et al.
Rethinking Tokenized Graph Transformers for Node Classification
Jinsong Chen, Chenyang Li, Gaichao Li et al.
Understanding Virtual Nodes: Oversquashing and Node Heterogeneity
Joshua Southern, Francesco Di Giovanni, Michael Bronstein et al.
Aligning Transformers with Weisfeiler-Leman
Luis Müller, Christopher Morris
CKGConv: General Graph Convolution with Continuous Kernels
Liheng Ma, Soumyasundar Pal, Yitian Zhang et al.
Comparing Graph Transformers via Positional Encodings
Mitchell Black, Zhengchao Wan, Gal Mishne et al.
Graph Positional and Structural Encoder
Semih Cantürk, Renming Liu, Olivier Lapointe-Gagné et al.
Less is More: on the Over-Globalizing Problem in Graph Transformers
Yujie Xing, Xiao Wang, Yibo Li et al.
On the Expressive Power of Spectral Invariant Graph Neural Networks
Bohang Zhang, Lingxiao Zhao, Haggai Maron
Recurrent Distance Filtering for Graph Representation Learning
Yuhui Ding, Antonio Orvieto, Bobby He et al.
Subgraphormer: Unifying Subgraph GNNs and Graph Transformers via Graph Products
Guy Bar Shalom, Beatrice Bevilacqua, Haggai Maron
Triplet Interaction Improves Graph Transformers: Accurate Molecular Graph Learning with Triplet Graph Transformers
Md Shamim Hussain, Mohammed Zaki, Dharmashankar Subramanian
What Improves the Generalization of Graph Transformers? A Theoretical Dive into the Self-attention and Positional Encoding
Hongkang Li, Meng Wang, Tengfei Ma et al.