Document detail
ID
oai:arXiv.org:2411.01532
Topic
Computer Science - Machine Learnin... Statistics - Machine LearningAuthor
Jacobs, Yahel Dayan, Reut Shaham, UriCategory
Computer Science
Year
2024
listing date
2/26/2025
Keywords
methods learningAbstract
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.
Jacobs, Yahel,Dayan, Reut,Shaham, Uri, 2024, SPARC: Spectral Architectures Tackling the Cold-Start Problem in Graph Learning
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