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.

Date
Jul 12, 2023
Event
NetSci 2023
Location
Vienna, Austria

More talk details will be added soon.

Yujie Zeng
Yujie Zeng
PhD Candidate

My research interests primarily focus on network science, higher-order network analysis, graph representation learning and interdisciplinary applications.

Yiming Huang
Yiming Huang
Undergraduate

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

Linyuan Lü
Linyuan Lü
Professor

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

Related