Continual learning on graph data has recently attracted paramount attention for its aim to
resolve the catastrophic forgetting problem on existing tasks while adapting the sequentially …

Memory replay based techniques have shown great success for continual learning with
incrementally accumulated Euclidean data. Directly applying them to continually expanding …

Graph filters are a staple tool for processing signals over graphs in a multitude of
downstream tasks. However, they are commonly designed for graphs with a fixed number of …

Memory replay based techniques have shown great success for continual learning with
incrementally accumulated Euclidean data. Directly applying them to continually expanding …

This paper addresses the problem of online network topology inference for expanding
graphs from a stream of spatiotemporal signals. Online algorithms for dynamic graph …

Simplicial convolutional filters can process signals defined over levels of a simplicial
complex such as nodes, edges, triangles, and so on with applications in eg, flow prediction …

Current spatiotemporal learning methods for complex data exploit the graph structure as an
inductive bias to restrict the function space and improve data and computation efficiency …