Kdd 2023 Transferable Graph Structure

Media Summary: Yilun Jin, Hong Kong University of Science and Technology In this video, we would like to briefly introduce our work, Huizhao Wang, Hikvision Research Institute Considering that each node has its own characteristics, we believe William Shiao, University of California, Riverside.

Kdd 2023 Transferable Graph Structure - Detailed Analysis & Overview

Yilun Jin, Hong Kong University of Science and Technology In this video, we would like to briefly introduce our work, Huizhao Wang, Hikvision Research Institute Considering that each node has its own characteristics, we believe William Shiao, University of California, Riverside. Hewen Wang, National University of Singapore. Yunbo Hou:School of Software and Microelectronics, Peking University;Haoran Ye:State Key Laboratory of General Artificial ... Jure Leskovec, Stanford University Innovation Award Talk.

Wentao Zhao, Shanghai Jiao Tong University. Gaotang Li, University of Michigan, Ann Arbor. A video presentation of Fanchen Bu and Kijung Shin, "On Improving the Cohesiveness of Yeping Hu, Lawrence Livermore National Laboratory Dynamic systems, encompassing everything from chaotic systems to ... Jihong Wang, Xi'an Jiaotong University EGIB: How to explain pretrained GNNs with fine-grained information in the representation ...

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KDD 2023 - Transferable Graph Structure Learning for Graph-based Traffic Forecasting Across Cities

KDD 2023 - Graph Structure Learning via Progressive Strategy

KDD 2023 - Clustering-Accelerated Representation Learning on Graphs

KDD 2023 - Efficient and Effective Edge-wise Graph Representation Learning

KDD 2025 - TransPlace: Transferable Circuit Global Placement via Graph Neural Network

KDD 2023 - Graphs, Databases and Machine Learning

KDD 2023 - Universal and Generalizable Structure Learning for Graph Neural Networks

KDD 2023 - Interpretable Sparsification of Brain Graphs"

KDD 2023 - Efficient and Joint Hyperparameter and Architecture Search for Collaborative Filtering

On Improving the Cohesiveness of Graphs by Merging Nodes (KDD 2023)

KDD 2023 - Rethinking Homophily in Graph Contrastive Learning

KDD 2023 - Graph Learning in Physical-informed Mesh-reduced Space for Real-world Dynamic Systems

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KDD 2023 - Transferable Graph Structure Learning for Graph-based Traffic Forecasting Across Cities

KDD 2023 - Transferable Graph Structure Learning for Graph-based Traffic Forecasting Across Cities

Yilun Jin, Hong Kong University of Science and Technology In this video, we would like to briefly introduce our work,

KDD 2023 - Graph Structure Learning via Progressive Strategy

KDD 2023 - Graph Structure Learning via Progressive Strategy

Huizhao Wang, Hikvision Research Institute Considering that each node has its own characteristics, we believe

KDD 2023 - Clustering-Accelerated Representation Learning on Graphs

KDD 2023 - Clustering-Accelerated Representation Learning on Graphs

William Shiao, University of California, Riverside.

KDD 2023 - Efficient and Effective Edge-wise Graph Representation Learning

KDD 2023 - Efficient and Effective Edge-wise Graph Representation Learning

Hewen Wang, National University of Singapore.

KDD 2025 - TransPlace: Transferable Circuit Global Placement via Graph Neural Network

KDD 2025 - TransPlace: Transferable Circuit Global Placement via Graph Neural Network

Yunbo Hou:School of Software and Microelectronics, Peking University;Haoran Ye:State Key Laboratory of General Artificial ...

KDD 2023 - Graphs, Databases and Machine Learning

KDD 2023 - Graphs, Databases and Machine Learning

Jure Leskovec, Stanford University Innovation Award Talk.

KDD 2023 - Universal and Generalizable Structure Learning for Graph Neural Networks

KDD 2023 - Universal and Generalizable Structure Learning for Graph Neural Networks

Wentao Zhao, Shanghai Jiao Tong University.

KDD 2023 - Interpretable Sparsification of Brain Graphs"

KDD 2023 - Interpretable Sparsification of Brain Graphs"

Gaotang Li, University of Michigan, Ann Arbor.

KDD 2023 - Efficient and Joint Hyperparameter and Architecture Search for Collaborative Filtering

KDD 2023 - Efficient and Joint Hyperparameter and Architecture Search for Collaborative Filtering

Yan Wen, Tsinghua University.

On Improving the Cohesiveness of Graphs by Merging Nodes (KDD 2023)

On Improving the Cohesiveness of Graphs by Merging Nodes (KDD 2023)

A video presentation of Fanchen Bu and Kijung Shin, "On Improving the Cohesiveness of

KDD 2023 - Rethinking Homophily in Graph Contrastive Learning

KDD 2023 - Rethinking Homophily in Graph Contrastive Learning

Wen-Zhi Li, Sun Yat-sen University.

KDD 2023 - Graph Learning in Physical-informed Mesh-reduced Space for Real-world Dynamic Systems

KDD 2023 - Graph Learning in Physical-informed Mesh-reduced Space for Real-world Dynamic Systems

Yeping Hu, Lawrence Livermore National Laboratory Dynamic systems, encompassing everything from chaotic systems to ...

KDD 2023 - Empower Post-hoc Graph Explanations with Information Bottleneck

KDD 2023 - Empower Post-hoc Graph Explanations with Information Bottleneck

Jihong Wang, Xi'an Jiaotong University EGIB: How to explain pretrained GNNs with fine-grained information in the representation ...