Study tests minute-scale rug-pull detection for new Solana tokens
Transfer graphs for 2,700 SolRPDS tokens already differ in size, density, and centralization within 12.5 minutes.
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17 stories, newest first · one card per story
Stories tagged #graph-neural-networks
FiLSim scales graph similarity with GNNs
Framework scores 150,000-node graphs in seconds while baselines fail.
arXiv proposes differentially private framework for GraphRAG
RelGuard is the first differentially private framework for GraphRAG providing formal privacy guarantees.
GraphMuon optimizes graph neural networks with matrix-aware updates
GraphMuon achieves 1.32x-2.42x speedups over AdamW on large OGB benchmarks.
Recurrent graph network lowers QC-MDPC decoding failures in McEliece
On 20 unseen matrix topologies, R-BGNN cut the decoding failure rate 63.7% at noise level t=10.
EnerGNN learns energy functions for exact constrained combinatorial inference
On a cell-therapy supply-chain task it reports a 0.05% mean primal gap and a 17.4× MIP speedup.
Quantum and classical models compared on dark-web link prediction
A four-qubit classifier stayed near chance under degree control, while VGAE reached mean ROC-AUC about 0.91.
Fragment-level graphs match atom-level molecular accuracy on smaller inputs
Integrated Gradients recovered known pharmacophores at 1.5 times chance on BACE and 3.2 times on PTR1.
SCARCE learns label-free graph representations robust to distribution shift
A two-stage autoencoder confuses environment signals while preserving reconstruction and leads label-free results on DrugOOD.
Distilling Graph Geometry: Knowledge Gap from GNNs to MLPs
Researchers propose G2MLP, a curvature-guided distillation method that transfers GNN geometry into graph-free MLPs.
EDiS: Edge Disjoint Subgraph Sparsification Framework for Graph Neural Networks
EDiS sparsifies GNN training by caching edge-disjoint subgraphs and recomposing them each epoch under an edge budget.
Effective Resistance and Graph Neural Network Reliability in Tissue-Specific Interactomes
Effective resistance in 24 tissue interactomes mostly tracks inverse degree, limiting GNN reliability signals.
A Unified Uncertainty Representation for Graph Neural Networks via Doubly-Spectral Stochastic Expansion
DSS-GNN unifies GNN calibration, OOD detection, and shift robustness in one spectral representation.
Diffusion-Induced Spatial Attention Overlapping Community Detection
Researchers present DISCO, using diffusion priors and sparse attention for overlapping community detection and network anomalies.
SEMA-GUARD: Semantic and Graph-Based Vulnerability Detection in Assembly Code
SEMA-GUARD uses semantic analysis and graph neural networks to detect vulnerabilities in assembly code, achieving 85.1% accuracy on a Juliet-derived benchmark.
Recurrent GraphNeural NetworkswithSet-BasedAggregation
Paper proves two-directional equivalence between recurrent GNNs with set-based aggregation and Boolean closure of reachability/safety properties in modal mu-calculus, checkable from weights.
Learning with Covariance Matrices: Principal Component Analysis Meets Learning with Graphs
Theoretical tutorial establishes equivalence between covariance neural networks and PCA, with stability and transferability bounds and brain-age applications.