arXiv:2507. 19702v1 Announce Type: cross Abstract: Identifying influential nodes in complex networks is a critical task with a wide range of applications across different domains.
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arXiv:2602.09716v2 Announce Type: replace Abstract: Computing the importance of nodes in networks is a long-standing fundamental problem that has driven extensive study of various centrality measures...
arXiv:2607. 09372v1 Announce Type: new Abstract: Graph Neural Networks (GNNs) provide a learning-based framework for approximating graph quantities that are expensive to compute exactly.
The paper introduces an unsupervised graph neural network framework for solving the Minimum Dominating Set problem in social networks. By training on 12,000 synthetic graphs, the method achieves up to 55× faster inference than metaheuristic baselines and 14× faster than supervised approaches while producing optimal or near‑optimal dominating sets on real‑world benchmarks. The learned heuristic generalizes well to unseen graph distributions, indicating strong practical applicability for large‑scale social network analysis.
arXiv:2607. 03587v1 Announce Type: new Abstract: We propose NetinfoGC, a framework for graph classification that extends the Network Usable Information (NUI) paradigm to graph-level learning.
arXiv:2608.29001v1 Announce Type: new Abstract: In a data-driven world, efficiently organizing and mapping relationships between objects is crucial. Graphs are powerful tools for modeling these conne...
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.