Search Results for author: Binchi Zhang

Found 7 papers, 5 papers with code

ELEGANT: Certified Defense on the Fairness of Graph Neural Networks

1 code implementation5 Nov 2023 Yushun Dong, Binchi Zhang, Hanghang Tong, Jundong Li

Graph Neural Networks (GNNs) have emerged as a prominent graph learning model in various graph-based tasks over the years.

Fairness Graph Learning

Adversarial Attacks on Fairness of Graph Neural Networks

1 code implementation20 Oct 2023 Binchi Zhang, Yushun Dong, Chen Chen, Yada Zhu, Minnan Luo, Jundong Li

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 applications.

Fairness

RELIANT: Fair Knowledge Distillation for Graph Neural Networks

1 code implementation3 Jan 2023 Yushun Dong, Binchi Zhang, Yiling Yuan, Na Zou, Qi Wang, Jundong Li

Knowledge Distillation (KD) is a common solution to compress GNNs, where a light-weighted model (i. e., the student model) is encouraged to mimic the behavior of a computationally expensive GNN (i. e., the teacher GNN model).

Fairness Graph Learning +1

AHEAD: A Triple Attention Based Heterogeneous Graph Anomaly Detection Approach

1 code implementation17 Aug 2022 Shujie Yang, Binchi Zhang, Shangbin Feng, Zhaoxuan Tan, Qinghua Zheng, Jun Zhou, Minnan Luo

In light of this problem, we propose AHEAD: a heterogeneity-aware unsupervised graph anomaly detection approach based on the encoder-decoder framework.

Attribute Graph Anomaly Detection

Federated Graph Machine Learning: A Survey of Concepts, Techniques, and Applications

no code implementations24 Jul 2022 Xingbo Fu, Binchi Zhang, Yushun Dong, Chen Chen, Jundong Li

Federated Graph Machine Learning (FGML) is a promising solution to tackle this challenge by training graph machine learning models in a federated manner.

BIG-bench Machine Learning

PPSGCN: A Privacy-Preserving Subgraph Sampling Based Distributed GCN Training Method

no code implementations22 Oct 2021 Binchi Zhang, Minnan Luo, Shangbin Feng, Ziqi Liu, Jun Zhou, Qinghua Zheng

In light of these problems, we propose a Privacy-Preserving Subgraph sampling based distributed GCN training method (PPSGCN), which preserves data privacy and significantly cuts back on communication and memory overhead.

Federated Learning Graph Learning +2

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