中文标题#
透過因果邊分離和譜方法解決圖異常檢測
英文标题#
Addressing Graph Anomaly Detection via Causal Edge Separation and Spectrum
中文摘要#
在現實世界中，異常實體通常會添加更多合法連接，同時隱藏與其他異常實體的直接鏈接，導致異常網絡中出現異質結構，而大多數基於圖神經網絡（GNN）的技術無法解決這一問題。 已在空間域提出了一些方法來解決這個問題。 然而，這些方法忽略了節點結構編碼、節點特徵及其上下文環境之間的複雜關係，並依賴於原則性指導，因此在解決譜域異質性問題方面的研究仍然有限。 本研究分析了不同異質程度節點的譜分佈，並發現異常節點的異質性會導致譜能量從低頻向高頻轉移。 為了解決上述挑戰，我們提出了一種基於因果邊分離的譜神經網絡 CES2-GAD，用於異質圖上的異常檢測。 首先，CES2-GAD 將原始圖通過因果干預分為同質邊和異質邊。 隨後，使用各種混合譜濾波器從分段圖中捕獲信號。 最後，將多個信號的表示進行拼接並輸入分類器以預測異常。 與真實數據集的大量實驗已證明了我們提出的方法的有效性。
英文摘要#
In the real world, anomalous entities often add more legitimate connections while hiding direct links with other anomalous entities, leading to heterophilic structures in anomalous networks that most GNN-based techniques fail to address. Several works have been proposed to tackle this issue in the spatial domain. However, these methods overlook the complex relationships between node structure encoding, node features, and their contextual environment and rely on principled guidance, research on solving spectral domain heterophilic problems remains limited. This study analyzes the spectral distribution of nodes with different heterophilic degrees and discovers that the heterophily of anomalous nodes causes the spectral energy to shift from low to high frequencies. To address the above challenges, we propose a spectral neural network CES2-GAD based on causal edge separation for anomaly detection on heterophilic graphs. Firstly, CES2-GAD will separate the original graph into homophilic and heterophilic edges using causal interventions. Subsequently, various hybrid-spectrum filters are used to capture signals from the segmented graphs. Finally, representations from multiple signals are concatenated and input into a classifier to predict anomalies. Extensive experiments with real-world datasets have proven the effectiveness of the method we proposed.
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