Neighbour-level Message Interaction Encoding for Improved Representation Learning on Graphs
School of Electrical and Data Engineering, Faculty of Engineering and Information Technology, University of Technology Sydney（悉尼科技大学工程与信息技术学院工程与数据工程学院）
Message passing has become the dominant framework in graph representation learning. The essential idea of the message-passing framework is to update node embeddings based on the information aggregated from local neighbours. However, most existing aggregation methods have not encoded neighbour-level message interactions into the aggregated message, resulting in an information lost in embedding generation. And this information lost could be accumulated and become more serious as more layers are added to the graph network model. To address this issue, we propose a neighbour-level message interaction information encoding method for improving graph representation learning. For messages that are aggregated at a node, we explicitly generate an encoding between each message and the rest messages using an encoding function. Then we aggregate these learned encodings and take the sum of the aggregated encoding and the aggregated message to update the embedding for the node. By this way, neighbour-level message interaction information is integrated into the generated node embeddings. The proposed encoding method is a generic method which can be integrated into message-passing graph convolutional networks. Extensive experiments are conducted on six popular benchmark datasets across four highly-demanded tasks. The results show that integrating neighbour-level message interactions achieves improved performance of the base models, advancing the state of the art results for representation learning over graphs.
Differential Encoding for Improved Representation Learning over Graphs
School of Electrical and Data Engineering, Faculty of Engineering and Information Technology, University of Technology Sydney（电子与数据工程学院，工程与信息技术学院，新南威尔士大学）
Learning from the Dark: Boosting Graph Convolutional Neural Networks with Diverse Negative Samples
The Australian Artificial Intelligence Institute (AAII), University of Technology Sydney（悉尼科技大学澳大利亚人工智能研究所（AAII））；Australian Catholic University（澳大利亚天主教大学）
Learning Harmonic Molecular Representations on Riemannian Manifold
ByteDance Research（字节跳动研究院）；University of Wisconsin-Madison（威斯康星大学麦迪逊分校）；Institute for AI Industry Research (AIR), Tsinghua University（清华大学人工智能产业研究院（AIR））
Multi-Scale Harmonic Encoding for Feature-Wise Graph Message Passing
School of Mathematics and the Data Science Institute, Shandong University（数学学院和数据科学研究院，山东大学）；School of Mathematics, Shandong University（数学学院，山东大学）；Data Science Institute, Shandong University（数据科学研究院，山东大学）
Neighbourhood Transformer: Switchable Attention for Monophily-Aware Graph Learning
Laboratory of Intelligent Collaborative Computing, University of Electronic Science and Technology of China（电子科技大学智能协同计算实验室）；School of Computer Science and Engineering, University of Electronic Science and Technology of China（电子科技大学计算机科学与工程学院）；Ubiquitous Intelligence and Trusted Services Key Laboratory of Sichuan Province（四川省泛在智能与可信服务重点实验室）
Facebook AI Research（Facebook人工智能研究院）；ENS - EHESS - PSL Research University - CNRS - INRIA（巴黎高等师范学院-社会科学高等研究院-巴黎文理研究大学-法国国家科学研究中心-法国国家信息与自动化研究所）；ICREA（加泰罗尼亚研究与高等教育学院）
LEAP: Local ECT-Based Learnable Positional Encodings for Graphs
AIDOS Lab, University of Fribourg（弗里堡大学AIDOS实验室）；Institute of AI for Health, Helmholtz Munich（慕尼黑亥姆霍兹人工智能与健康研究所）；Technical University of Munich（慕尼黑技术大学）；Department of Computer Science, ETH Zurich（苏黎世联邦理工学院计算机科学系）；Department of Computer Science, University of Basel（巴塞尔大学计算机科学系）
Graph Inductive Biases in Transformers without Message Passing
McGill University（麦吉尔大学）；University of Oxford（牛津大学）；Massachusetts Institute of Technology（麻省理工学院）；MetaAI（Meta人工智能实验室）；Mila - Quebec AI Institute（米拉-魁北克人工智能研究所）；Canada CIFAR AI Chair（加拿大CIFAR人工智能讲席）；FiveAI（FiveAI公司）；International Laboratory on Learning Systems（国际学习系统实验室）
本文提出 GRIT，通过相对位置编码、节点对注意力和度信息注入，在无消息传递的情况下赋予 Graph Transformer 图归纳偏置，并取得理论表达力与 SOTA 性能。
Generalized Laplacian Positional Encoding for Graph Representation Learning
Ludwig-Maximilian University of Munich（慕尼黑大学）；MILA（蒙特利尔学习算法研究所）；New Jersey Institute of Technology（新泽西理工学院）；TU Wien（维也纳工业大学）；Massachusetts Institute of Technology（麻省理工学院）；University of Vienna（维也纳大学）；NVIDIA Research（英伟达研究院）
How Powerful are K-hop Message Passing Graph Neural Networks
Washington University in St. Louis（圣路易斯华盛顿大学）；Washington University School of Medicine（华盛顿大学医学院）；Peking University（北京大学）；Beijing Institute for General Artificial Intelligence（北京通用人工智能研究院）
A framework for the emergence and analysis of language in social learning agents
Johannes Gutenberg-University Mainz（美因茨约翰内斯·古腾堡大学）；Technical University Darmstadt（达姆施塔特工业大学）；University of Bonn Medical Center（波恩大学医学中心）；Max-Planck Institute for Brain Research（马克斯·普朗克大脑研究所）
