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Found 8 papers, 5 papers with code
Addressing Shortcomings in Fair Graph Learning Datasets: Towards a New Benchmark
These datasets are thoughtfully designed to include relevant graph structures and bias information crucial for the fair evaluation of models.
Simplified PCNet with Robustness
Graph Neural Networks (GNNs) have garnered significant attention for their success in learning the representation of homophilic or heterophilic graphs.
CDC: A Simple Framework for Complex Data Clustering
In particular, we deploy CDC to graph data of size 111M.
Provable Filter for Real-world Graph Clustering
Motivated by this finding, we construct two graphs that are highly homophilic and heterophilic, respectively.
PC-Conv: Unifying Homophily and Heterophily with Two-fold Filtering
This is attributed to their neglect of homophily in heterophilic graphs, and vice versa.
Upper Bounding Barlow Twins: A Novel Filter for Multi-Relational Clustering
To overcome this drawback, we propose to learn a graph filter motivated by the theoretical analysis of Barlow Twins.
Revisiting Link Prediction: A Data Perspective
We recognize three fundamental factors critical to link prediction: local structural proximity, global structural proximity, and feature proximity.
REQA: Coarse-to-fine Assessment of Image Quality to Alleviate the Range Effect
The reason for the range effect is that the predicted deviations both in a wide range and in a narrow range destroy the uniformity between MOS and pMOS.
