Grunnleggende konsepter
Developing a powerful GNN model that can ensure performance under both homophily and heterophily is crucial for efficient graph learning.
Sammendrag
Graph neural networks (GNNs) have shown significant performance in semi-supervised node classification.
Existing GNNs struggle to achieve optimal node representations due to the constraints of undirected graphs.
AMUD quantifies the relationship between node profiles and topology, offering valuable insights for adaptively modeling directed graphs.
ADPA introduces a new directed graph learning paradigm, achieving effective message aggregation.
Extensive experiments on benchmark datasets substantiate the impressive performance of ADPA.
Statistikk
最も重要なトポロジーのスコアは0.814です。
AMUDによるスコアは0.705で、有向グラフに適したモデルを示しています。
ADPAのスコアは0.814で、効果的なメッセージ集約を実現しています。
Sitater
"Undirected GNNs are more suitable for handling homophilous undirected graphs, while directed GNNs exhibit a significant advantage in dealing with heterophilous digraphs."
"Modeling directed information assists directed GNNs in capturing intricate heterophily while discarding directed information is more crucial for utilizing homophily."