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Found 14 papers, 8 papers with code
Federated Graph Learning with Graphless Clients
To enable structure knowledge transfer, we design a GNN model and a feature encoder on each client.
Federated Graph Learning with Structure Proxy Alignment
Moreover, FGL also encounters a unique challenge for the node classification task: the nodes from a minority class in a client are more likely to have biased neighboring information, which prevents FGL from learning expressive node embeddings with Graph Neural Networks (GNNs).
Understanding and Modeling Job Marketplace with Pretrained Language Models
Job marketplace is a heterogeneous graph composed of interactions among members (job-seekers), companies, and jobs.
Towards Certified Unlearning for Deep Neural Networks
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.
Verification of Machine Unlearning is Fragile
These strategies enable data owners to ascertain whether their target data has been effectively unlearned from the model.
IDEA: A Flexible Framework of Certified Unlearning for Graph Neural Networks
Specifically, we first instantiate four types of unlearning requests on graphs, and then we propose an approximation approach to flexibly handle these unlearning requests over diverse GNNs.
Safety in Graph Machine Learning: Threats and Safeguards
In this survey paper, we explore three critical aspects vital for enhancing safety in Graph ML: reliability, generalizability, and confidentiality.
ELEGANT: Certified Defense on the Fairness of Graph Neural Networks
Graph Neural Networks (GNNs) have emerged as a prominent graph learning model in various graph-based tasks over the years.
Adversarial Attacks on Fairness of Graph Neural Networks
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.
RELIANT: Fair Knowledge Distillation for Graph Neural Networks
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).
Federated Graph Machine Learning: A Survey of Concepts, Techniques, and Applications
Federated Graph Machine Learning (FGML) is a promising solution to tackle this challenge by training graph machine learning models in a federated manner.
TwiBot-22: Towards Graph-Based Twitter Bot Detection
Twitter bot detection has become an increasingly important task to combat misinformation, facilitate social media moderation, and preserve the integrity of the online discourse.
Tackling the Local Bias in Federated Graph Learning
To solve this problem, we propose a novel FGL framework to make the local models similar to the model trained in a centralized setting.
