PyTorch

Investigating GNN Expressiveness in Graph Generation Tasks
Imperial UROP 2023 Student Project – We propose an approach to improve GNN-based graph generative models including GCPN, GraphAF and GraphEBM, and investigate the correlation between GNN expressiveness in the graph prediction and graph generation contexts.
Xiandong Zou, Xiangyu Zhao, Pietro Liò, Yiren Zhao
21 Aug 2023
Investigating GNN Expressiveness in Graph Generation Tasks
Imperial UROP 2023 Student Project – We propose an approach to improve GNN-based graph generative models including GCPN, GraphAF and GraphEBM, and investigate the correlation between GNN expressiveness in the graph prediction and graph generation contexts.
Xiandong Zou, Xiangyu Zhao, Pietro Liò, Yiren Zhao
Will More Expressive Graph Neural Networks do Better on Generative Tasks?
We propose an approach to improve GNN-based graph generative models including GCPN, GraphAF and GraphEBM, and investigate the correlation between GNN expressiveness in the graph prediction and graph generation contexts.
Xiandong Zou, Xiangyu Zhao, Pietro Liò, Yiren Zhao
Hybrid Graph: A Unified Graph Representation with Datasets and Benchmarks for Complex Graphs
We introduce the concept of hybrid graphs, a unified definition for higher-order graphs, and present the Hybrid Graph Benchmark (HGB), a collection of hybrid graph datasets with an extensible evaluation framework.
Zehui Li, Xiangyu Zhao, Mingzhu Shen, Guy-Bart Stan, Pietro Liò, Yiren Zhao
Unifying Higher-Order Graph Representation with New Datasets and Benchmarks
PhD First Year Project – We introduce the concept of hybrid graphs, a unified definition for higher-order graphs, and present the Hybrid Graph Benchmark (HGB), a collection of hybrid graph datasets with an extensible evaluation framework.
Zehui Li, Xiangyu Zhao, Mingzhu Shen, Guy-Bart Stan, Pietro Liò, Yiren Zhao
8 Jun 2023
Unifying Higher-Order Graph Representation with New Datasets and Benchmarks
PhD First Year Project – We introduce the concept of hybrid graphs, a unified definition for higher-order graphs, and present the Hybrid Graph Benchmark (HGB), a collection of hybrid graph datasets with an extensible evaluation framework.
Zehui Li, Xiangyu Zhao, Mingzhu Shen, Guy-Bart Stan, Pietro Liò, Yiren Zhao
Task-Agnostic Graph Neural Network Evaluation via Adversarial Collaboration
We propose GraphAC (Graph Adversarial Collaboration) – a conceptually novel, principled, task-agnostic, and stable framework for evaluating GNNs through contrastive self-supervision, without the need of handcrafted augmentations.
Xiangyu Zhao, Hannes Stärk, Dominique Beaini, Yiren Zhao, Pietro Liò
Task-Agnostic Graph Neural Network Evaluation via Adversarial Collaboration
Master’s Dissertation Presentation.
8 Jun 2022 9:10 AM — 1:00 PM Computer Laboratory, University of Cambridge
Xiangyu Zhao
Task-Agnostic Graph Neural Network Evaluation via Adversarial Collaboration
Task-Agnostic Graph Neural Network Evaluation via Adversarial Collaboration
Master’s Dissertation supervised by Prof Pietro Liò, Dr Dominique Beaini and Hannes Stärk.
Xiangyu Zhao, Hannes Stärk, Dominique Beaini, Yiren Zhao, Pietro Liò
27 May 2022
Task-Agnostic Graph Neural Network Evaluation via Adversarial Collaboration
Master’s Dissertation supervised by Prof Pietro Liò, Dr Dominique Beaini and Hannes Stärk.
Xiangyu Zhao, Hannes Stärk, Dominique Beaini, Yiren Zhao, Pietro Liò