[論文レビュー] Graph Neural Networks for Graphs with Heterophily: A Survey

Xin Zheng, Wang, Yi+6 more|arXiv (Cornell University)|Feb 14, 2022

Advanced Graph Neural Networks被引用数 87

ひとこと要約

本調査は、異質性グラフ上のGNNの包括的な分類法とベンチマークを提供し、非局所的隣接拡張とアーキテクチャ精練アプローチを詳述し、今後の方向性を示す。

ABSTRACT

Recent years have witnessed fast developments of graph neural networks (GNNs) that have benefited myriads of graph analytic tasks and applications. In general, most GNNs depend on the homophily assumption that nodes belonging to the same class are more likely to be connected. However, as a ubiquitous graph property in numerous real-world scenarios, heterophily, i.e., nodes with different labels tend to be linked, significantly limits the performance of tailor-made homophilic GNNs. Hence, GNNs for heterophilic graphs are gaining increasing research attention to enhance graph learning with heterophily. In this paper, we provide a comprehensive review of GNNs for heterophilic graphs. Specifically, we propose a systematic taxonomy that essentially governs existing heterophilic GNN models, along with a general summary and detailed analysis. Furthermore, we discuss the correlation between graph heterophily and various graph research domains, aiming to facilitate the development of more effective GNNs across a spectrum of practical applications and learning tasks in the graph research community. In the end, we point out the potential directions to advance and stimulate more future research and applications on heterophilic graph learning with GNNs.

研究の動機と目的

• ホモフィー仮定が成り立たない異質性グラフ上のGNNの研究を動機づける。
• 非局所隣接拡張とGNNアーキテクチャ精練に分類される異質性GNNアプローチを系統的に分類する分類法を提供する。
• 公正な評価を支えるための実世界のベンチマークと異質性グラフのデータセットを要約する。
• 解釈性、頑健性、スケーラビリティ、データ探索の観点で制限を分析し、今後の方向性を提案する。

提案手法

• 隣接ノードの定義方法とメッセージの集約方法に基づいて、異質性GNNの系統的分類法を提案する。
• 二つの主要カテゴリをレビューする: 非局所隣接拡張（高次隣接の混合と潜在的隣接の発見）とGNNアーキテクチャ精練（適応的集約、ego-neighbor分離、層間結合）。
• 代表的なモデルと技術を要約し、スペクトルおよび空間的集約スキームやメッセージ中の異質性対処法などを含む。
• 実世界の異質性ベンチマークとその統計を収集・要約して堅牢な評価を支援する。

実験結果

リサーチクエスチョン

• RQ1 What are the current method families for GNNs on heterophilic graphs and how do they address neighbor discovery and information aggregation?
• RQ2 How do non-local neighbor extension and GNN architectural refinements compare and complement each other in heterophilic settings?
• RQ3 What benchmarks exist for heterophilic GNN evaluation and what limitations do they have?
• RQ4 What directions are most promising for improving interpretability, robustness, scalability, and data exploration in heterophilic GNNs?

主な発見

• A systematic taxonomy divides heterophilic GNNs into non-local neighbor extension and GNN architecture refinement.
• Non-local methods include high-order neighbor mixing and potential neighbor discovery to capture informative distant nodes.
• Architecture refinement methods cover adaptive aggregation, ego-neighbor separation, and inter-layer combination to enhance discriminability.
• A set of real-world heterophilic benchmarks (e.g., WebKB subsets, Chameleon, Squirrel, Wiki, ArXiv-Year, Snap-Patents, etc.) are summarized to support evaluation.
• The survey highlights interpretability, robustness, scalability, and comprehensive benchmarks as key future directions for heterophilic GNN research.