G-SPARC: SPectral ARchitectures tackling the Cold-start problem in Graph learning

• URL: http://arxiv.org/abs/2411.01532v1
• Date: Sun, 03 Nov 2024 11:39:09 GMT
• Title: G-SPARC: SPectral ARchitectures tackling the Cold-start problem in Graph learning
• Authors: Yahel Jacobs, Reut Dayan, Uri Shaham,
• Abstract summary: We propose G-SPARC, a novel framework addressing cold-start nodes. By utilizing a key idea of transitioning from graph representation to spectral representation, our approach is generalizable to cold-start nodes. Our method outperforms existing models on cold-start nodes across various tasks like node classification, node clustering, and link prediction.
• Score: 3.870455775654713
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: Graphs play a central role in modeling complex relationships across various domains. Most graph learning methods rely heavily on neighborhood information, raising the question of how to handle cold-start nodes - nodes with no known connections within the graph. These models often overlook the cold-start nodes, making them ineffective for real-world scenarios. To tackle this, we propose G-SPARC, a novel framework addressing cold-start nodes, that leverages generalizable spectral embedding. This framework enables extension to state-of-the-art methods making them suitable for practical applications. By utilizing a key idea of transitioning from graph representation to spectral representation, our approach is generalizable to cold-start nodes, capturing the global structure of the graph without relying on adjacency data. Experimental results demonstrate that our method outperforms existing models on cold-start nodes across various tasks like node classification, node clustering, and link prediction. G-SPARC provides a breakthrough built-in solution to the cold-start problem in graph learning. Our code will be publicly available upon acceptance.

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