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Found 4 papers, 1 papers with code
GCNH: A Simple Method For Representation Learning On Heterophilous Graphs
Yet, achievement of consistent GNN performance on heterophilous graphs remains an open research problem.
Ranked #7 on
Node Classification
on Cornell
Node Classification on Non-Homophilic (Heterophilic) Graphs
Representation Learning
2-hop Neighbor Class Similarity (2NCS): A graph structural metric indicative of graph neural network performance
In this work, we highlight the limitations of the widely used homophily ratio and the recent Cross-Class Neighborhood Similarity (CCNS) metric in estimating GNN performance.
What Will I Do Next? The Intention from Motion Experiment
In this paper, we bridge cognitive and computer vision studies, by demonstrating the effectiveness of video-based approaches for the prediction of human intentions.
Predicting Human Intentions from Motion Only: A 2D+3D Fusion Approach
In this paper, we address the new problem of the prediction of human intents.
