Tag: Graph Algorithms
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SCGNN: Enhancing Graph Neural Networks with Granular-ball Computing
Discover SCGNN, a novel graph neural network method using granular-ball computing to boost semantic consistency and reduce noise in graph learning.
Skyline-First Traversal for Efficient Multi-Criteria Graph Search
Discover how skyline-first traversal optimizes multi-criteria graph search using Pareto dominance for efficient path evaluation and deterministic progress.
ReLU Networks for Precise Graph Generation Within Constraints
Discover how ReLU neural networks generate graphs within exact edit distance limits, ensuring validity without relying on training data.
Optimizing GNN Architectures for Entity Resolution in MDM
Discover a hierarchy of GNN expressivity for efficient entity resolution in master data management, optimizing model complexity and performance.
Cost-Sensitive Aggregation in Heterophilous Graphs with CSNA
Explore how Cost-Sensitive Neighborhood Aggregation improves graph neural networks on heterophilous graphs and when per-edge routing is effective.