Enriching Graph Topology Representations with Line Graph Transformations

Many Graph Neural Networks (GNNs) in the literature are based on message-passing, which introduces a strong learning bias that may fail to capture critical relational information encoded in the edges of the graph, particularly in tasks where the structural role of edges is as significant as that of nodes, such as in chemical molecular analysis or social network dynamics. We propose a novel architecture inspired by line graph theory that explicitly models edge adjacencies, iteratively transforming a graph into its corresponding line graph. Differently from message-passing, the iterative application of this transformation enables the exchange of information among non-adjacent nodes, allowing for the capture of complex topological dependencies, which standard GNNs overlook. Experiments on standard benchmarks show promising results.