ACM SIGKDD(国际数据挖掘与知识发现大会,简称KDD)会议始于1989年,是数据挖掘领域历史最悠久、规模最大的国际顶级学术会议,也是首个引入大数据、数据科学、预测分析、众包等概念的会议,每年吸引了大量数据挖掘、机器学习、大数据和人工智能等领域的研究学者、从业人员参与。
AMiner通过AI技术,对 KDD2023 收录的会议论文进行了分类整理,今日分享的是GNN主题论文!(由于篇幅关系,本篇只展现部分论文,点击文末链接可直达KDD顶会页面,查看所有论文)
1.Spatial Heterophily Aware Graph Neural Networks
链接:https://www.aminer.cn/pub/6493c733d68f896efad19bfa/
2.All in One: Multi-task Prompting for Graph Neural Networks
链接:https://www.aminer.cn/pub/64a63bbad68f896efaec478f/
3.GraphGLOW: Universal and Generalizable Structure Learning for Graph Neural Networks
链接:https://www.aminer.cn/pub/64927546d68f896efa88a31b/
4.Learning Strong Graph Neural Networks with Weak Information
链接:https://www.aminer.cn/pub/6476d20cd68f896efaf727d4/
5.Graph Neural Bandits
链接:https://www.aminer.cn/pub/6433f6b990e50fcafd6ef10f/
6.WinGNN: Dynamic Graph Neural Networks with Random Gradient Aggregation Window
链接:https://www.aminer.cn/pub/64af9a093fda6d7f065a6ec4/
7.E-commerce Search via Content Collaborative Graph Neural Network
链接:https://www.aminer.cn/pub/64af9a033fda6d7f065a6982/
8.Localised Adaptive Spatial-Temporal Graph Neural Network
链接:https://www.aminer.cn/pub/64af9a073fda6d7f065a6d31/
9.Leveraging Relational Graph Neural Network for Transductive Model Ensemble
链接:https://www.aminer.cn/pub/64af9a0a3fda6d7f065a6fdd/
10.Narrow the Input Mismatch in Deep Graph Neural Network Distillation
链接:https://www.aminer.cn/pub/64af9a013fda6d7f065a6752/
11.Certified Edge Unlearning for Graph Neural Networks
链接:https://www.aminer.cn/pub/64af9a013fda6d7f065a66e3/
12.Enhancing Node-Level Adversarial Defenses by Lipschitz Regularization of Graph Neural Networks
链接:https://www.aminer.cn/pub/64af9a093fda6d7f065a6f7b/
13.When to Pre-Train Graph Neural Networks? From Data Generation Perspective!
链接:https://www.aminer.cn/pub/64af9a0a3fda6d7f065a6fe9/
14.Towards graph-level anomaly detection via deep evolutionary mapping
链接:https://www.aminer.cn/pub/6433f67c90e50fcafd6da665/
15.MixupExplainer: Generalizing Explanations for Graph Neural Networks with Data Augmentation
链接:https://www.aminer.cn/pub/64af99fd3fda6d7f065a6347/
16.Pyramid Graph Neural Network: A Graph Sampling and Filtering Approach for Multi-scale Disentangled Representations
链接:https://www.aminer.cn/pub/64af99fd3fda6d7f065a6396/
17.MGNN: Graph Neural Networks Inspired by Distance Geometry Problem
链接:https://www.aminer.cn/pub/64af9a003fda6d7f065a6623/
18.Augmenting Recurrent Graph Neural Networks with a Cache
链接:https://www.aminer.cn/pub/64af9a013fda6d7f065a66fb/
19.AdaProp: Learning Adaptive Propagation for Graph Neural Network based Knowledge Graph Reasoning
链接:https://www.aminer.cn/pub/64af9a013fda6d7f065a67cf/
20.Financial Default Prediction via Motif-preserving Graph Neural Network with Curriculum Learning
链接:https://www.aminer.cn/pub/64af9a083fda6d7f065a6dff/
21.QTIAH-GNN: Quantity and Topology Imbalance-aware Heterogeneous Graph Neural Network for Bankruptcy Prediction
链接:https://www.aminer.cn/pub/64af9a083fda6d7f065a6e1d/
22.Multiplex Heterogeneous Graph Neural Network with Behavior Pattern Modeling
链接:https://www.aminer.cn/pub/64af9a083fda6d7f065a6e2a/
23.Interpretable Sparsification of Brain Graphs: Better Practices and Effective Designs for Graph Neural Networks
链接:https://www.aminer.cn/pub/64af9a093fda6d7f065a6f27/
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