Sep 20, 2021 · The core concept of GNNs is to find a representation by recursively aggregating the representations of a central node and those of its neighbor, ...
The core concept of GNNs is to find a representation by recursively aggregating the representations of a central node and those of its neighbor, and its success ...
However, it remains a big challenge dealing with non-grid or irregular data such as e.g. protein-interaction networks, social networks, and knowledge graphs ...
Further research is being performed in the field of Graph Neural Network (GNN) modeling to identify feature correlations. Feature correlation aggregation is ...
This is empirically verified across a broad set of benchmarks, surpassing previous state-of-the-art results by a significant margin (e.g., 33.116% improvement ...
Sep 10, 2024 · The core concept of GNNs is to find a representation by recursively aggregating the representations of a central node and those of its neighbor, ...
Feature Correlation Aggregation: on the Path to Better Graph Neural Networks. J. Zhou, T. Zhang, P. Fang, L. Petersson, and M. Harandi. DICTA, page 356-363.
In this paper, we propose a new perspective to look at the performance degradation of deep GNNs, i.e., feature overcorrelation. Through empirical and ...
The attentive walk aggregation in AWA-GNN allows for more expressive power and enhanced representation learning in graph-structured data. This technique has ...
Application of graph neural network and feature information ...
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The graph neural network is used to introduce external KGs for information aggregation and merge features. It is different from the existing direct connection ...