153 citations · 158 across the 6 of their papers we have counts for
6 papers
Hypergraphs with Edge-Dependent Vertex Weights: p-Laplacians and Spectral Clustering
Yu Zhu, Santiago Segarra
We study p-Laplacians and spectral clustering for a recently proposed hypergraph model that incorporates edge-dependent vertex weights (EDVW). These weights can reflect different i…
Signal processing on simplicial complexes
Michael T. Schaub, Jean-Baptiste Seby, Florian Frantzen +3
Higher-order networks have so far been considered primarily in the context of studying the structure of complex systems, i.e., the higher-order or multi-way relations connecting th…
An Impossibility Theorem for Node Embedding
T. Mitchell Roddenberry, Yu Zhu, Santiago Segarra
With the increasing popularity of graph-based methods for dimensionality reduction and representation learning, node embedding functions have become important objects of study in t…
Free Energy Node Embedding via Generalized Skip-gram with Negative Sampling
Yu Zhu, Ananthram Swami, Santiago Segarra
A widely established set of unsupervised node embedding methods can be interpreted as consisting of two distinctive steps: i) the definition of a similarity matrix based on the gra…
Co-clustering Vertices and Hyperedges via Spectral Hypergraph Partitioning
Yu Zhu, Boning Li, Santiago Segarra
We propose a novel method to co-cluster the vertices and hyperedges of hypergraphs with edge-dependent vertex weights (EDVWs). In this hypergraph model, the contribution of every v…
Signal Processing on Higher-Order Networks: Livin' on the Edge ... and Beyond
Michael T. Schaub, Yu Zhu, Jean-Baptiste Seby +2
In this tutorial, we provide a didactic treatment of the emerging topic of signal processing on higher-order networks. Drawing analogies from discrete and graph signal processing,…