47 citations · 65 across the 21 of their papers we have counts for
15 papers · 1 filter
A Survey of Weight Space Learning: Understanding, Representation, and Generation
Xiaolong Han, Zehong Wang, Bo Zhao +8
Neural network weights are typically viewed as the end product of training, while most deep learning research focuses on data, features, and architectures. However, recent advances…
GraphTOP: Graph Topology-Oriented Prompting for Graph Neural Networks
Xingbo Fu, Zhenyu Lei, Zihan Chen +3
Graph Neural Networks (GNNs) have revolutionized the field of graph learning by learning expressive graph representations from massive graph data. As a common pattern to train powe…
Beyond the Permutation Symmetry of Transformers: The Role of Rotation for Model Fusion
Binchi Zhang, Zaiyi Zheng, Zhengzhang Chen +1
Symmetry in the parameter space of deep neural networks (DNNs) has proven beneficial for various deep learning applications. A well-known example is the permutation symmetry in Mul…
Virtual Nodes Can Help: Tackling Distribution Shifts in Federated Graph Learning
Xingbo Fu, Zihan Chen, Yinhan He +4
Federated Graph Learning (FGL) enables multiple clients to jointly train powerful graph learning models, e.g., Graph Neural Networks (GNNs), without sharing their local graph data…
Federated Graph Learning with Graphless Clients
Xingbo Fu, Song Wang, Yushun Dong +3
Federated Graph Learning (FGL) is tasked with training machine learning models, such as Graph Neural Networks (GNNs), for multiple clients, each with its own graph data. Existing m…
Federated Graph Learning with Structure Proxy Alignment
Xingbo Fu, Zihan Chen, Binchi Zhang +2
Federated Graph Learning (FGL) aims to learn graph learning models over graph data distributed in multiple data owners, which has been applied in various applications such as socia…
Verification of Machine Unlearning is Fragile
Binchi Zhang, Zihan Chen, Cong Shen +1
As privacy concerns escalate in the realm of machine learning, data owners now have the option to utilize machine unlearning to remove their data from machine learning models, foll…
Towards Certified Unlearning for Deep Neural Networks
Binchi Zhang, Yushun Dong, Tianhao Wang +1
In the field of machine unlearning, certified unlearning has been extensively studied in convex machine learning models due to its high efficiency and strong theoretical guarantees…
IDEA: A Flexible Framework of Certified Unlearning for Graph Neural Networks
Yushun Dong, Binchi Zhang, Zhenyu Lei +2
Graph Neural Networks (GNNs) have been increasingly deployed in a plethora of applications. However, the graph data used for training may contain sensitive personal information of…
Safety in Graph Machine Learning: Threats and Safeguards
Song Wang, Yushun Dong, Binchi Zhang +7
Graph Machine Learning (Graph ML) has witnessed substantial advancements in recent years. With their remarkable ability to process graph-structured data, Graph ML techniques have b…
Certified Defense on the Fairness of Graph Neural Networks
Yushun Dong, Binchi Zhang, Hanghang Tong +1
Graph Neural Networks (GNNs) have emerged as a prominent graph learning model in various graph-based tasks over the years. Nevertheless, due to the vulnerabilities of GNNs, it has…
Adversarial Attacks on Fairness of Graph Neural Networks
Binchi Zhang, Yushun Dong, Chen Chen +3
Fairness-aware graph neural networks (GNNs) have gained a surge of attention as they can reduce the bias of predictions on any demographic group (e.g., female) in graph-based appli…
RELIANT: Fair Knowledge Distillation for Graph Neural Networks
Yushun Dong, Binchi Zhang, Yiling Yuan +3
Graph Neural Networks (GNNs) have shown satisfying performance on various graph learning tasks. To achieve better fitting capability, most GNNs are with a large number of parameter…
Federated Graph Machine Learning: A Survey of Concepts, Techniques, and Applications
Xingbo Fu, Binchi Zhang, Yushun Dong +2
Graph machine learning has gained great attention in both academia and industry recently. Most of the graph machine learning models, such as Graph Neural Networks (GNNs), are train…
Tackling the Local Bias in Federated Graph Learning
Binchi Zhang, Minnan Luo, Shangbin Feng +3
Federated graph learning (FGL) has become an important research topic in response to the increasing scale and the distributed nature of graph-structured data in the real world. In…