Recent years have witnessed the popularity and success of graph neural networks (GNN) in various scenarios. To obtain data-specific GNN architectures, researchers turn to neural architecture search (NAS), which has made impressive success in discovering effective architectures in convolutional neural networks. However, it is non-trivial to apply NAS approaches to GNN due to challenges in search space design and the expensive searching cost of existing NAS methods. In this work, to obtain the data-specific GNN architectures and address the computational challenges facing by NAS approaches, we propose a framework, which tries to Search to Aggregate NEighborhood (SANE), to automatically design data-specific GNN architectures. By designing a novel and expressive search space, we propose a differentiable search algorithm, which is more efficient than previous reinforcement learning based methods. Experimental results on four tasks and seven real-world datasets demonstrate the superiority of SANE compared to existing GNN models and NAS approaches in terms of effectiveness and efficiency. (Code is available at: https://github.com/AutoML-4Paradigm/SANE).
Meta-Aggregator: Learning to Aggregate for 1-bit Graph Neural Networks
The University of Sydney（悉尼大学）；Stevens Institute of Technology（斯蒂文斯理工学院）；National University of Singapore（新加坡国立大学）；Zhejiang University（浙江大学）；JD Explore Academy（京东探索学院）
Learning Robust Representations with Graph Denoising Policy Network
East China Normal University（华东师范大学）；University of California Los Angeles（加州大学洛杉矶分校）；NEC Laboratories America, Inc.（美国NEC实验室）；Georgia Institute of Technology（佐治亚理工学院）
Balans: Multi-Armed Bandits-based Adaptive Large Neighborhood Search for Mixed-Integer Programming Problem
Department of Computer Science, University of Southern California（南加州大学计算机科学系）；AI Center of Excellence, Fidelity Investments（富达投资人工智能卓越中心）；Department of Computer Science, Brown University（布朗大学计算机科学系）
Efficient Neural Neighborhood Search for Pickup and Delivery Problems
National University of Singapore（新加坡国立大学）；Singapore Institute of Manufacturing Technology, A*STAR（新加坡科技研究局制造技术研究所）；Institute of Marine Science and Technology, Shandong University（山东大学海洋科学与技术学院）；Institute for Infocomm Research, A*STAR（新加坡科技研究局资讯通信研究院）；South China University of Technology（华南理工大学）
ParBalans: Parallel Multi-Armed Bandits-based Adaptive Large Neighborhood Search
Department of Mechanical & Industrial Engineering, Northeastern University（东北大学机械与工业工程系）；AI Center of Excellence, Fidelity Investments（fidelity investments 精英研究中心）；Department of Computer Science, University of Southern California（南加州大学计算机科学系）；Department of Computer Science, Brown University（布朗大学计算机科学系）
Learning Large Neighborhood Search for Vehicle Routing in Airport Ground Handling
Nanyang Technological University（南洋理工大学）；Eindhoven University of Technology（埃因霍温理工大学）；Singapore Management University（新加坡管理大学）；Shandong University（山东大学）；Agency for Science Technology and Research (A*STAR)（新加坡科技研究局）
HyP-ASO: A Hybrid Policy-based Adaptive Search Optimization Framework for Large-Scale Integer Linear Programs
Ning Xu School of Mathematics and Statistics Nanjing University of Science and Technology（南京理工大学数学与统计学学院）；Junkai Zhang Institute of Automation Chinese Academy of Sciences（中国科学院自动化研究所）；Huigen Ye Department of Computer Science and Technology Tsinghua University（清华大学计算机科学与技术系）；Yang Wu Institute of Automation Chinese Academy of Sciences（中国科学院自动化研究所）；Hua Xu Department of Computer Science and Technology Tsinghua University（清华大学计算机科学与技术系）；Huiling Xu School of Mathematics and Statistics Nanjing University of Science and Technology（南京理工大学数学与统计学学院）；Yifan Zhang Institute of Automation, Chinese Academy of Sciences（中国科学院自动化研究所）
New Adaptive Mechanism for Large Neighborhood Search using Dual Actor-Critic
School of Intelligent Science and Technology, Nanjing University of Science and Technology（智能科学与技术学院，南京理工大学）；Department of Mechanical Engineering, North China Electric Power University, Baoding 071003, China（机械工程系，华北电力大学，保定071003，中国）；EM Normandie Business School, Métis Lab, 92110, Clichy, France（EM诺曼底商学院，Métis实验室，92110，法国）；Université Paris-Saclay, CentraleSupélec, Laboratoire Génie Industriel, 91190, Gif-sur-Yvette, France（巴黎萨克雷大学，CentraleSupélec，工业工程实验室，91190，法国）
