カリキュラムベースのトレーニングを通じてサイングラフニューラルネットワークの強化
Zeyu Zhang1, Lu Li1, Xingyu Ji1
1the College of the Informatics, Huazhong Agricultural University, China.
まとめ
この研究は,シグネッテッド・グラフ・ニューラル・ネットワーク (SGNNs) の新しいカリキュラムの学習枠組みを導入し,難易度によって並べられたエッジのトレーニングによってモデルの精度と安定性を改善します. CSGフレームワークは,SGNNの実世界の署名グラフデータに対するパフォーマンスを向上させます.
科学分野:
- グラフ理論
- 機械学習
- ネットワーク科学
背景:
- シグネチャーグラフは ポジティブ・ネガティブ・コネクションで 複雑な関係をモデル化します
- 署名グラフニューラルネットワーク (SGNN) は,署名グラフを分析するための新興ツールです.
- 現在のSGNNの訓練は,ランダムなサンプルオーダーを使用する構造化されたアプローチが欠けている.
研究 の 目的:
- SGNNの専門的な訓練計画を策定する.
- シグネチャー・グラフの 学習困難を解決するためです
- SGNNモデルの性能と安定性を向上させる.
主な方法:
- 署名グラフ (CSG) のカリキュラム表現学習枠組みを提案した.
- サイングラフのエッジを測定する軽量な難易度計を開発した.
- SGNNのトレーニングサンプルを簡単から難しいまで注文するスケジュラーを実装しました.
主要な成果:
- 一般的なSGNNモデルの精度が23.7%まで向上しました.
- 標準偏差を8.4%減らし モデルの安定性を改善しました
- 経験的に検証された 六つの現実の世界で署名されたグラフデータセット
結論:
- カリキュラムの学習は,訓練のサンプル順序を最適化することで,SGNNに著しく利益をもたらします.
- CSGの枠組みは,SGNNの訓練のための実用的で効果的な方法を提供します.
- 提案されたアプローチは,より正確で安定したサイングラフ表現の学習につながります.
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