Influential Simplices Mining via Simplicial Convolutional Network

Abstract

Simplicial complexes play a critical role in higher-order network analysis due to their heterogeneity, but identifying influential simplices remains elusive. This paper proposes the influential simplices mining neural network (ISMnet), which uses higher-order presentations and graph convolutional operators to identify vital simplices of arbitrary order. Empirical results demonstrate that ISMnet significantly outperforms existing methods in ranking nodes and 2-simplices, making it a potent tool in higher-order network analysis.

Yujie Zeng

Undergraduate

My research interests include network science, higher-order network and vital node identification.

Yiming Huang

Undergraduate

My research interests include network science, vital node identification, and topological deep learning.

Linyuan Lü

Professor

Professor, doctoral supervisor, winner of the National Natural Science Foundation of Outstanding Youth Science Fund.