3 accepted papers
Representation learning is central to graph machine learning, powering tasks such as link prediction and node classification. However, most graph embeddings are hard to interpret, offering limited insight into how learned features relate to graph structure. Many networks naturally admit a role-mixtu…
Autoencoders based on Graph Neural Networks (GNNs) have garnered significant attention in recent years for their ability to learn informative latent representations of complex topologies, such as graphs. Despite the prevalence of Graph Autoencoders, there has been limited focus on developing and eva…
Automatically extracting robust representations from large and complex time series data is becoming imperative for several real-world applications. Unfortunately, the potential of common neural network architectures in capturing invariant properties of time series remains relatively underexplored. F…