International Conference on Computer and Knowledge Engineering

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Robust Learning to Learn Graph Topologies

Authors :

Navid Akhavan Attar¹ Ali Fahim²

1- University of Tehran 2- University of Tehran

Keywords :

Network Science،Machine Learning،Graph Learning،Neural Networks،Complex Systems،Learning to Learn Graph Topologies،Graph Topology

Abstract :

Graph learning and revealing the relationship between agents is an essential and pivotal problem in network science and machine learning, carrying significant implications across a broad range of applications. In the literature, there are generally two approaches to graph learning: model-based and learning-based methods. Recent learning-based methods like L2G, GLAD, and uGLAD add more flexibility to model structural priors in graph learning. We propose a novel approach, RL2G, which offers robustness to the “Learning to Learn Graph Topologies” method. This allows us to recover the topologies of graphs using a pre-trained model, independently from their size and topological properties. The experiments conducted on both synthetic and real-world data provide strong evidence that our approach performs well in revealing the relationships between different data entities.