Media Summary: Ruizhong Qiu, University of Illinois Urbana-Champaign. Hewen Wang, National University of Singapore. Xujia Li, Hong Kong University of Science and Technology.

Kdd 2023 Reconstructing Graph Diffusion - Detailed Analysis & Overview

Ruizhong Qiu, University of Illinois Urbana-Champaign. Hewen Wang, National University of Singapore. Xujia Li, Hong Kong University of Science and Technology. Xinyue Hu, The University of Texas at Arlington. Yeping Hu, Lawrence Livermore National Laboratory Dynamic systems, encompassing everything from chaotic systems to ... William Shiao, University of California, Riverside.

Gaotang Li, University of Michigan, Ann Arbor. Jinhua Zhu, University of Science and Technology of China. A video presentation of Fanchen Bu and Kijung Shin, "On Improving the Cohesiveness of Shichao Pei, The University of Notre Dame This video presents a novel framework to alleviate the impact of the intractable ...

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KDD 2023 - Reconstructing Graph Diffusion History from a Single Snapsho
KDD 2023 - Efficient and Effective Edge-wise Graph Representation Learning
KDD 2023 - Diga: Guided Diffusion Model for Graph Recovery in Anti-Money Laundering
KDD 2023 - Rethinking Homophily in Graph Contrastive Learning
KDD 2023 - Imputation-based Series Anomaly DetectionConditional Weight-Incremental Diffusion Models
KDD 2023 - Expert Knowledge-Aware Image Difference Graph Representation Learning
KDD 2023 - Graph Learning in Physical-informed Mesh-reduced Space for Real-world Dynamic Systems
KDD 2023 - Clustering-Accelerated Representation Learning on Graphs
KDD 2023 - Interpretable Sparsification of Brain Graphs"
KDD 2023 - Dual-view Molecular Pre-training
On Improving the Cohesiveness of Graphs by Merging Nodes (KDD 2023)
KDD 2023 - Knowledge Graph Self-Supervised Rationalization for Recommendation
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KDD 2023 - Reconstructing Graph Diffusion History from a Single Snapsho

KDD 2023 - Reconstructing Graph Diffusion History from a Single Snapsho

Ruizhong Qiu, University of Illinois Urbana-Champaign.

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 2023 - Diga: Guided Diffusion Model for Graph Recovery in Anti-Money Laundering

KDD 2023 - Diga: Guided Diffusion Model for Graph Recovery in Anti-Money Laundering

Xujia Li, Hong Kong University of Science and Technology.

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 - Imputation-based Series Anomaly DetectionConditional Weight-Incremental Diffusion Models

KDD 2023 - Imputation-based Series Anomaly DetectionConditional Weight-Incremental Diffusion Models

Zehua Gou, Henan Univeristy.

KDD 2023 - Expert Knowledge-Aware Image Difference Graph Representation Learning

KDD 2023 - Expert Knowledge-Aware Image Difference Graph Representation Learning

Xinyue Hu, The University of Texas at Arlington.

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 - Clustering-Accelerated Representation Learning on Graphs

KDD 2023 - Clustering-Accelerated Representation Learning on Graphs

William Shiao, University of California, Riverside.

KDD 2023 - Interpretable Sparsification of Brain Graphs"

KDD 2023 - Interpretable Sparsification of Brain Graphs"

Gaotang Li, University of Michigan, Ann Arbor.

KDD 2023 - Dual-view Molecular Pre-training

KDD 2023 - Dual-view Molecular Pre-training

Jinhua Zhu, University of Science and Technology of China.

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 - Knowledge Graph Self-Supervised Rationalization for Recommendation

KDD 2023 - Knowledge Graph Self-Supervised Rationalization for Recommendation

Yuhao Yang, The University of Hong Kong.

KDD 2023 - Fewshot Low-resource Knowledge Graph Completion with Multi-view Representation Generation

KDD 2023 - Fewshot Low-resource Knowledge Graph Completion with Multi-view Representation Generation

Shichao Pei, The University of Notre Dame This video presents a novel framework to alleviate the impact of the intractable ...