Signal and information processing over graphs

Pooling Strategies for Simplicial Convolutional Networks

Efficient Learning of Balanced Signature Graphs

Revisit Sampling Theory of Bandlimited Graph Signals: One Bridge Between GSP and DSP

Simplicial Vector Autoregressive Model for Streaming Edge Flows

Convolutional Filtering on Sampled Manifolds

Estimating and Analyzing Neural Information Flow Using Signal Processing on Graphs

Kernel Ridge Regression for Generalized Graph Signal Processing

Sparse Graph Learning with Spectrum Prior for Deep Graph Convolutional Networks

Blind Polynomial Regression

GRAPH LEARNING FROM GAUSSIAN AND STATIONARY GRAPH SIGNALS