Search Results for author:
Found 17 papers, 13 papers with code
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.
GiGaMAE: Generalizable Graph Masked Autoencoder via Collaborative Latent Space Reconstruction
By considering embeddings encompassing graph topology and attribute information as reconstruction targets, our model could capture more generalized and comprehensive knowledge.
When Newer is Not Better: Does Deep Learning Really Benefit Recommendation From Implicit Feedback?
In recent years, neural models have been repeatedly touted to exhibit state-of-the-art performance in recommendation.
Spatial-Temporal Networks for Antibiogram Pattern Prediction
In this paper, we propose a novel problem of antibiogram pattern prediction that aims to predict which patterns will appear in the future.
Few-shot Node Classification with Extremely Weak Supervision
Recent few-shot node classification methods typically learn from classes with abundant labeled nodes (i. e., meta-training classes) and then generalize to classes with limited labeled nodes (i. e., meta-test classes).
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).
Interpreting Unfairness in Graph Neural Networks via Training Node Attribution
In this paper, we study a novel problem of interpreting GNN unfairness through attributing it to the influence of training nodes.
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.
On Structural Explanation of Bias in Graph Neural Networks
The low transparency on how the structure of the input network influences the bias in GNN outcome largely limits the safe adoption of GNNs in various decision-critical scenarios.
Improving Fairness in Graph Neural Networks via Mitigating Sensitive Attribute Leakage
Motivated by our analysis, we propose Fair View Graph Neural Network (FairVGNN) to generate fair views of features by automatically identifying and masking sensitive-correlated features considering correlation variation after feature propagation.
FAITH: Few-Shot Graph Classification with Hierarchical Task Graphs
Specifically, these works propose to accumulate meta-knowledge across diverse meta-training tasks, and then generalize such meta-knowledge to the target task with a disjoint label set.
Empowering Next POI Recommendation with Multi-Relational Modeling
Noticeably, LBSNs have offered unparalleled access to abundant heterogeneous relational information about users and POIs (including user-user social relations, such as families or colleagues; and user-POI visiting relations).
Fairness in Graph Mining: A Survey
Recently, algorithmic fairness has been extensively studied in graph-based applications.
EDITS: Modeling and Mitigating Data Bias for Graph Neural Networks
We then develop a framework EDITS to mitigate the bias in attributed networks while maintaining the performance of GNNs in downstream tasks.
Assessing the Causal Impact of COVID-19 Related Policies on Outbreak Dynamics: A Case Study in the US
Besides, as the confounders may be time-varying during COVID-19 (e. g., vigilance of residents changes in the course of the pandemic), it is even more difficult to capture them.
AdaGNN: Graph Neural Networks with Adaptive Frequency Response Filter
Existing efforts can be mainly categorized as spectral-based and spatial-based methods.
