Title: Multi‑view Adaptive Partitioning with Global Association for Graph Anomaly Detection
Primary area: learning on graphs and other geometries & topologies
Keywords: Graph Anomaly Detection; Representation Learning; Graph Global Association

Abstract: Graph anomaly detection has emerged as a fundamental technique for detecting anomalies in complex relational data across diverse domains.
A key challenge in graph anomaly detection is the difficulty deep models face in representing tabular node attributes. Unlike image and text data, whose samples usually reside on a single, smooth, and differentiable manifold, tabular samples scatter across fragmented manifolds lacking manifold connectedness. This fragmentation violates the local-smoothness assumption that most deep networks rely on, leading to degraded performance. 
To address the above challenge, a Multi-view Adaptive Partition Encoder (MAPE) is proposed. Multiple complementary adaptive partition operators are introduced in MAPE to discretize the feature space and assign learnable embeddings to the resulting subspaces, thereby reducing manifold connectedness.
Furthermore, sharing a sub-space is treated as evidence of high-order affinity between nodes, forming the basis of the proposed Multi-Pattern Global Association (MPGA) module for capturing global dependencies.
Extensive experiments across 10 benchmarks demonstrate that the proposed method consistently outperforms 27 competitive baselines, including recent state‑of‑the‑art models.