arXiv Machine Learning
SHAKE-GNN: Scalable Hierarchical Kirchhoff-Forest Graph Neural Network
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arXiv Machine Learning
Aug 18
Not All Neighbors Matter: Understanding the Impact of Graph Sparsification on GNN Pipelines
arXiv:2603. 06952v2 Announce Type: replace Abstract: As graphs scale to billions of nodes and edges, graph Machine Learning workloads are constrained by the cost of multi-hop traversals over exponentially growing neighborhoods.
arXiv Machine Learning
Jul 31
Train Small, Deploy Large: Zero-Shot GNN Transfer Through Geometric Renormalization
arXiv:2607. 27767v1 Announce Type: new Abstract: Graph neural networks (GNNs) can operate on large graphs but become infrastructure-sensitive at the scale of millions of nodes and typically require scalable training techniques for even larger graphs.
arXiv Machine Learning
Jun 19
Towards Graph-Based Deep Learning for Map Generalization: Insights from Building Footprints Simplification and Aggregation
arXiv:2606. 19956v1 Announce Type: new Abstract: Map generalization remains one of the fundamental tasks in cartography, especially for the simplification and aggregation of complex building footprints.
arXiv Machine Learning
Sep 7
Physics-Aware Random Walk Fingerprints for Scalable Power Grid Graph Classification
The paper introduces Multi-Channel Physics-Aware Random Walk Fingerprints (MC-PA-RWF), a lightweight graph-level representation that incorporates physical edge states into random-walk propagation for power grid graphs. By constructing multiple edge-weighted channels from domain-relevant attributes and concatenating channel-specific fingerprints, the method achieves high balanced accuracy on PowerGraph benchmarks, outperforming topology-only RWF and matching or surpassing several graph neural network baselines. Experiments on three benchmark systems show statistically significant improvements, with the node-edge extension reaching up to 99.32% balanced accuracy and boosting failure-class F1 scores by 1.60–5.84 percentage points.
arXiv Machine Learning
Aug 27
DeltaGNN: Graph Neural Network with Information Flow Control
DeltaGNN introduces an information flow control mechanism that uses a new connectivity measure, the information flow score, to mitigate over‑smoothing and over‑squashing in Graph Neural Networks. This approach enables linear computational and memory overhead while effectively capturing both short‑range and long‑range node interactions. Experiments on ten diverse real‑world datasets demonstrate superior performance with limited computational complexity.
arXiv Machine Learning
Sep 10
Interpretable Network-assisted Random Forest+
The paper introduces Interpretable Network-assisted Random Forest+ (RF+), a family of flexible models that combine the predictive power of random forests with network information. It offers intrinsic interpretability by providing global and local feature importance measures, as well as sample influence metrics, allowing researchers to assess both feature effects and the contribution of network neighbors. The authors claim that RF+ achieves competitive prediction accuracy while remaining transparent, making it suitable for high-impact problems where understanding model decisions is crucial.