214
0
v1v2 (latest)

SPARC: Spectral Architectures Tackling the Cold-Start Problem in Graph Learning

Abstract

Graphs play a central role in modeling complex relationships in data, yet most graph learning methods falter when faced with cold-start nodes--new nodes lacking initial connections--due to their reliance on adjacency information. To tackle this, we propose SPARC, a groundbreaking framework that introduces a novel approach to graph learning by utilizing generalizable spectral embeddings. With a simple yet powerful enhancement, SPARC empowers state-of-the-art methods to make predictions on cold-start nodes effectively. By eliminating the need for adjacency information during inference and effectively capturing the graph's structure, we make these methods suitable for real-world scenarios where new nodes frequently appear. Experimental results demonstrate that our framework outperforms existing models on cold-start nodes across tasks such as node classification, node clustering, and link prediction. SPARC provides a solution to the cold-start problem, advancing the field of graph learning.

View on arXiv
@article{jacobs2025_2411.01532,
  title={ SPARC: Spectral Architectures Tackling the Cold-Start Problem in Graph Learning },
  author={ Yahel Jacobs and Reut Dayan and Uri Shaham },
  journal={arXiv preprint arXiv:2411.01532},
  year={ 2025 }
}
Comments on this paper

We use cookies and other tracking technologies to improve your browsing experience on our website, to show you personalized content and targeted ads, to analyze our website traffic, and to understand where our visitors are coming from. See our policy.