Fairness-aware graph neural networks: A survey
Graph Neural Networks (GNNs) have become increasingly important due to their
representational power and state-of-the-art predictive performance on many fundamental …
representational power and state-of-the-art predictive performance on many fundamental …
Edge Classification on Graphs: New Directions in Topological Imbalance
Recent years have witnessed the remarkable success of applying Graph machine learning
(GML) to node/graph classification and link prediction. However, edge classification task that …
(GML) to node/graph classification and link prediction. However, edge classification task that …
A Novel Evaluation Perspective on GNNs-based Recommender Systems through the Topology of the User-Item Graph
Recently, graph neural networks (GNNs)-based recommender systems have encountered
great success in recommendation. As the number of GNNs approaches rises, some works …
great success in recommendation. As the number of GNNs approaches rises, some works …
Understanding the Generalizability of Link Predictors Under Distribution Shifts on Graphs
Recently, multiple models proposed for link prediction (LP) demonstrate impressive results
on benchmark datasets. However, many popular benchmark datasets often assume that …
on benchmark datasets. However, many popular benchmark datasets often assume that …
Data Quality-Aware Graph Machine Learning
Recent years have seen a significant shift in Artificial Intelligence from model-centric to data-
centric approaches, highlighted by the success of large foundational models. Following this …
centric approaches, highlighted by the success of large foundational models. Following this …
[PDF][PDF] Knowledge-centric Machine Learning on Graphs
Y Tian - 2024 - curate.nd.edu
Relational data, especially graphs where entities are represented as nodes and the
relations connecting them are denoted as edges, have become a common language for …
relations connecting them are denoted as edges, have become a common language for …