Abstract: Despite advancements using graph neural networks (GNNs) to capture complex user-item interactions, challenges persist due to data sparsity and noise. To address these, self-supervised ...

Abstract: Traditional machine-learning approaches face limitations when confronted with insufficient data. Transfer learning addresses this by leveraging knowledge from closely related domains. The ...

Does the intrinsic curvature of complex networks hold the key to unveiling graph anomalies that conventional approaches overlook? Reconstruction-based graph anomaly detection (GAD) methods overlook ...

Each line segment of a distance-time graph represents one part of a journey. A distance-time graph, sometimes referred to as a travel graph, is a way of representing a journey. It is helpful to have ...

This repository provides the official implementation for the paper: "SCENIR: Visual Semantic Clarity through Unsupervised Scene Graph Retrieval" by Nikolaos Chaidos, Angeliki Dimitriou, Maria ...