ACM SIGKDD 国际会议（ACM SIGKDD Conference on Knowledge Discovery and Data Mining，简称KDD）是由ACM的知识发现及数据挖掘专委会（SIGKDD）主办的数据挖掘研究领域的顶级年会。自1995年开始举办。KDD为来自学术界、企业界和政府部门的研究人员和数据挖掘从业者提供了学术交流和展示研究成果的理想场所。因为KDD大会涉及的议题大多跨学科且应用广泛，所以吸引了来自统计、机器学习、数据库、万维网、生物信息学、多媒体、自然语言处理、人机交互、社会网络计算、高性能计算以及大数据挖掘等众多领域的专家和学者。在本篇推送中，我们将会分享 KDD 2022中图机器学习研究领域的概述。
Source:KDD 2022 | Washington DC, U.S.
图和网络
论文标题：Graph Rationalization with Environment-based Augmentations
论文链接：https://arxiv.org/pdf/2206.02886.pdf
作者团队：Gang Liu, Tong Zhao, Jiaxin Xu, Tengfei Luo, Meng Jiang
论文标题：Geometer: Graph Few-Shot Class-Incremental Learning via Prototype Representation
论文链接：https://arxiv.org/pdf/2205.13954.pdf
作者团队：Bin Lu, Xiaoying Gan, Lina Yang, Weinan Zhang, Luoyi Fu, Xinbing Wang
论文标题：Minimizing Congestion for Balanced Dominators
论文链接：https://arxiv.org/pdf/2112.10973.pdf
作者团队：Yosuke Mizutani, Annie Staker, Blair D. Sullivan
论文标题：SMORE: KNOWLEDGE GRAPH COMPLETION AND MULTI-HOP REASONING
IN MASSIVE KNOWLEDGE GRAPHS
论文链接：https://arxiv.org/pdf/2110.14890.pdf
作者团队：Hongyu Ren，Hanjun Dai 2，Bo Dai，Xinyun Chen，Denny Zhou，Jure Leskovec，Dale Schuurmans
论文标题： FlowGEN: A Generative Model for Flow Graphs
论文链接：https://arxiv.org/pdf/2207.07656.pdf
作者团队：Aman Madaan，Yiming Yang
图的跨学科应用:生物学、气候和物理学
论文标题： Graph-in-Graph Network for Automatic Gene Ontology Description Generation
论文链接：https://arxiv.org/pdf/2206.05311.pdf
作者团队：Fenglin Liu, Bang Yang, Chenyu You, Xian Wu, Shen Ge, Adelaide Woicik, Sheng Wang
论文标题：Protein Representation Learning by Geometric Structure Pretraining
论文链接：https://arxiv.org/pdf/2203.06125.pdf
作者团队：Zuobai Zhang, Minghao Xu, Arian Jamasb, Vijil Chenthamarakshan, Aurelie Lozano, Payel Das, Jian Tang
论文标题：RetroGraph: Retrosynthetic Planning with Graph Search
论文链接：[2206.11477] RetroGraph: Retrosynthetic Planning with Graph Search
作者团队：Shufang Xie, Rui Yan, Peng Han, Yingce Xia, Lijun Wu, Chenjuan Guo, Bin Yang, Tao Qin
图的因果分析以及可解释性
论文标题： Variational Flow Graphical Model
论文链接：[2207.02722] Variational Flow Graphical Model
作者团队：Shaogang Ren, Belhal Karimi, Dingcheng Li, Ping Li
论文标题：ML4S: Learning Causal Skeleton from Vicinal Graphs
论文链接：https://www.microsoft.com/en-us/research/uploads/prod/2022/07/ML4S-camera-ready.pdf
作者团队：Pingchuan Ma，Rui Ding，Haoyue Dai，Yuanyuan Jiang， Shuai Wang
图模型架构
论文标题： Global Self-Attention as a Replacement for Graph Convolution
论文链接：[2108.03348] Global Self-Attention as a Replacement for Graph Convolution
作者团队：Md Shamim Hussain, Mohammed J. Zaki, Dharmashankar Subramanian
论文标题： ROLAND: Graph Learning Framework for Dynamic Graphs
论文链接：[2208.07239] ROLAND: Graph Learning Framework for Dynamic Graphs
作者团队：Jiaxuan You, Tianyu Du, Jure Leskovec
论文标题： Graph Neural Networks with Node-wise Architecture
论文链接：https://dl.acm.org/doi/pdf/10.1145/3534678.3539387
作者团队：Zhen WangZhewei WeiYaliang LiWeirui KuangBolin Ding
图挖掘
论文标题： Source Localization of Graph Diffusion via Variational Autoencoders for Graph Inverse Problems
论文链接：[2206.12327] Source Localization of Graph Diffusion via Variational Autoencoders for Graph Inverse Problems
作者团队：Chen Ling, Junji Jiang, Junxiang Wang, Liang Zhao
论文标题： Few-shot Heterogeneous Graph Learning via Cross-domain Knowledge Transfer
论文链接：https://dl.acm.org/doi/pdf/10.1145/3534678.3539431
作者团队：Qiannan ZhangXiaodong WuQiang YangChuxu ZhangXiangliang Zhang
论文标题： Towards an Optimal Asymmetric Graph Structure for Robust Semi-supervised Node Classification
论文链接：https://dl.acm.org/doi/abs/10.1145/3534678.3539332
作者团队：Zixing SongYifei ZhangIrwin King
论文标题： COSTA: Covariance-Preserving Feature Augmentation for Graph Contrastive Learning
论文链接：[2206.04726] COSTA: Covariance-Preserving Feature Augmentation for Graph Contrastive Learning
作者团队：Yifei Zhang, Hao Zhu, Zixing Song, Piotr Koniusz, Irwin King
论文标题：GUIDE: Group Equality Informed Individual Fairness in Graph Neural Networks
论文链接：http://www.ece.virginia.edu/~jl6qk/paper/KDD22_Group_Informed_Individual_Fairness.pdf
作者团队：Weihao Song, Jundong Li
论文标题： Graph Structural Attack by Spectral Distance
论文链接：[2111.00684] Graph Structural Attack by Spectral Distance
作者团队：Lu Lin, Ethan Blaser, Hongning Wang
论文标题： Feature Overcorrelation in Deep Graph Neural Networks: A New Perspective
论文链接：[2206.07743] Feature Overcorrelation in Deep Graph Neural Networks: A New Perspective
作者团队：Wei Jin, Xiaorui Liu, Yao Ma, Charu Aggarwal, Jiliang Tang
时空图序列和时空图数据
论文标题：Graph-Flashback Network for Next Location Recommendation
论文链接：https://dl.acm.org/doi/abs/10.1145/3534678.3539383
作者团队：Xuan RaoLisi ChenYong LiuShuo ShangBin YaoPeng Han
论文标题： Multi-Agent Graph Convolutional Reinforcement Learning for Dynamic Electric Vehicle Charging Pricing
论文链接：https://dl.acm.org/doi/abs/10.1145/3534678.3539416
作者团队：Weijia ZhangHao LiuJindong HanYong GeHui Xiong
论文标题：Mining Spatio-Temporal Relations via Self-Paced Graph Contrastive Learning
论文链接： https://dl.acm.org/doi/abs/10.1145/3534678.3539422
作者团队：Ronfan Li, Ting Zhong, Fan Zhou
论文标题： Learning the Evolutionary and Multi-scale Graph Structure for Multivariate Time Series Forecasting
论文链接： [2206.13816] Learning the Evolutionary and Multi-scale Graph Structure for Multivariate Time Series Forecasting
作者团队：Junchen Ye, Zihan Liu, Bowen Du, Leilei Sun, Weimiao Li, Yanjie Fu, Hui Xiong
论文标题： Pre-training Enhanced Spatial-temporal Graph Neural Network for Multivariate Time Series Forecasting
论文链接：[2206.09113] Pre-training Enhanced Spatial-temporal Graph Neural Network for Multivariate Time Series Forecasting
作者团队：Zezhi Shao, Zhao Zhang, Fei Wang, Yongjun Xu
论文标题： Learning the Evolutionary and Multi-scale Graph Structure for Multivariate Time Series Forecasting
论文链接： [2206.13816] Learning the Evolutionary and Multi-scale Graph Structure for Multivariate Time Series Forecasting
作者团队：Junchen Ye, Zihan Liu, Bowen Du, Leilei Sun, Weimiao Li, Yanjie Fu, Hui Xiong
图综合领域
论文标题： GraphMAE: Self-Supervised Masked Graph Autoencoders
论文链接： [2205.10803] GraphMAE: Self-Supervised Masked Graph Autoencoders
作者团队：Zhenyu Hou, Xiao Liu, Yukuo Cen, Yuxiao Dong, Hongxia Yang, Chunjie Wang, Jie Tang
论文标题： FreeKD: Free-direction Knowledge Distillation for Graph Neural Networks
论文链接： [2206.06561] FreeKD: Free-direction Knowledge Distillation for Graph Neural Networks
作者团队：Kaituo Feng, Changsheng Li, Ye Yuan, Guoren Wang
论文标题： Multi-Behavior Hypergraph-Enhanced Transformer for Sequential Recommendation
论文链接： [2207.05584] Multi-Behavior Hypergraph-Enhanced Transformer for Sequential Recommendation
作者团队：Yuhao Yang, Chao Huang, Lianghao Xia, Yuxuan Liang, Yanwei Yu, Chenliang Li
论文标题： Continuous-Time and Multi-Level Graph Representation Learning for Origin-Destination Demand Prediction
论文链接：[2206.15005] Continuous-Time and Multi-Level Graph Representation Learning for Origin-Destination Demand Prediction
作者团队：Liangzhe Han, Xiaojian Ma, Leilei Sun, Bowen Du, Yanjie Fu, Weifeng Lv, Hui Xiong
论文标题： GPPT: Graph Pre-training and Prompt Tuning to Generalize Graph Neural Networks
论文链接：https://dl.acm.org/doi/abs/10.1145/3534678.3539249
作者团队：Mingchen SunKaixiong ZhouXin HeYing WangXin Wang