newsReddit r/MachineLearningTrust 52 · CommunityPublished yesterdayLive · 15h ago

Your GNN is probably just an overcomplicated MLP (Tabular Leakage). We built SynthFin-AML to enforce strict causal boundaries. [P]

We noticed our anti-money laundering models were performing suspiciously well. After digging into standard baselines on dynamic graphs, we found widespread temporal leakage in message-passing. If you train a GNN on a static snapshot of a dynamic graph, your model is likely cheating by seeing future edges during training. We got sick of reviewing papers with broken evals, so we released SynthFin-AML v10.0 (100k nodes, 1.2M edges) to force strict causal bou

Why these links exist

Every edge carries a method, confidence, and the source snippet that justified it — so bad links are debuggable.

PossiblePossibly related (embedding) · 49%Query-Side Attacks on GNN-Based KGQA: Tracing Failures from Entity Linking to Answer Generation →
PossiblePossibly related (embedding) · 47%Beyond Sparse Supervision: Diffusion-Guided Learning for Few-Shot Graph Fraud Detection →
PossiblePossibly related (embedding) · 46%Blackknife: Hard-Label Query-Limited Black-Box Attacks on Heterogeneous Graph Neural Networks →

Covers

paperQuery-Side Attacks on GNN-Based KGQA: Tracing Failures from Entity Linking to Answer Generation paperBeyond Sparse Supervision: Diffusion-Guided Learning for Few-Shot Graph Fraud Detection paperBlackknife: Hard-Label Query-Limited Black-Box Attacks on Heterogeneous Graph Neural Networks

Related across the graph

paperQuery-Side Attacks on GNN-Based KGQA: Tracing Failures from Entity Linking to Answer Generation paperBlackknife: Hard-Label Query-Limited Black-Box Attacks on Heterogeneous Graph Neural Networks paperBeyond Sparse Supervision: Diffusion-Guided Learning for Few-Shot Graph Fraud Detection