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 …

Signals with an underlying irregular geometric structure are prevalent in modern
applications and are often well-represented using graphs. The field of graph signal …

Data processing tasks over graphs couple the data residing over the nodes with the
topology through graph signal processing tools. Graph filters are one such prominent tool …

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 …

Data processing over graphs is usually done on graphs of fixed size. However, graphs often
grow with new nodes arriving over time. Knowing the connectivity information of these …