Proposed a hypergraph neural network framework based on a tensor representation of the hypergraph structure.

 The paper is under a minor revision to IEEE Transaction on Neural Networks and Learning Systems

Proved the equivalence between hypergraph neural networks and signal denoising, proposed an iterative approach to address the over-smoothing problem.

 The paper was published on the European conference on signal processing.

Addressed the challenge of learning the underlying hypergraph topology from the data by assuming the data possesses a certain regularity or smoothness. Demonstrated the effectiveness of the learned hypergraph structure in hypergraph learning-convolutional neural networks (t-HyperGLNN).

 The paper was accepted by IEEE Transactions on Network Signal Processing over Networks

Proposed a hierarchical pooing and unpooling layer that acts as a backbone of hypergraph autoencoder, performed anomaly detection using reconstruction errors.

 The paper is in preparation.