AI Navigate
インサイト最新記事一覧AI大全

Geometric Evolution Graph Convolutional Networks: Enhancing Graph Representation Learning via Ricci Flow

arXiv cs.LG / 3/30/2026

💬 OpinionIdeas & Deep AnalysisModels & Research
Read original →
共有:

Key Points

  • The paper introduces the Geometric Evolution Graph Convolutional Network (GEGCN) to improve graph representation learning by explicitly modeling how a graph’s geometry evolves over time.
  • GEGCN uses an LSTM to capture a structural sequence produced by applying discrete Ricci flow, then incorporates these learned dynamic representations into a Graph Convolutional Network.
  • Experiments on multiple benchmark datasets show that GEGCN reaches state-of-the-art results for graph classification, indicating strong generalization across different graph settings.
  • The framework performs especially well on heterophilic graphs, where neighboring nodes tend to have different labels, suggesting it is effective for challenging relational structures.

Abstract

We introduce the Geometric Evolution Graph Convolutional Network (GEGCN), a novel framework that enhances graph representation learning by modeling geometric evolution on graphs. Specifically, GEGCN employs a Long Short-Term Memory to model the structural sequence generated by discrete Ricci flow, and the learned dynamic representations are infused into a Graph Convolutional Network. Extensive experiments demonstrate that GEGCN achieves state-of-the-art performance on classification tasks across various benchmark datasets, with its performance being particularly outstanding on heterophilic graphs.

関連おすすめサービス

※当サイトはアフィリエイト広告を利用しています
🎧

Notta搭載AI議事録イヤホン ZENCHORD1

AI時代の仕事術。Notta搭載で会議の議事録を自動生成するスマートイヤホン。

🎙️

AI搭載ボイスレコーダー Plaud

世界100万人が愛用。AIで文字起こし・要約を自動化するボイスレコーダー。

🖼️

画像高画質化AIツール Aiarty Image Enhancer

AIで画像を高画質化。写真・イラストを簡単にアップスケール。