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

Yahel Jacobs, Reut Dayan, Uri Shaham

Keywords: Cold-Start Nodes, Graph Neural Networks, Spectral Representation

TL;DR: A novel framework using generalizable spectral embeddings to effectively address the cold-start problem in graph learning, enabling adaptable extension for accurate predictions on nodes with no connections.

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 \textit{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.

Primary Area: learning on graphs and other geometries & topologies

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Submission Number: 6516