4 citations · 10 across the 19 of their papers we have counts for
4 papers · 1 filter
Data-Driven Graph Filters via Adaptive Spectral Shaping
Dylan Sandfelder, Mihai Cucuringu, Xiaowen Dong
We introduce Adaptive Spectral Shaping, a data-driven framework for graph filtering that learns a reusable baseline spectral kernel and modulates it with a small set of Gaussian fa…
On the Stability of Graph Convolutional Neural Networks: A Probabilistic Perspective
Ning Zhang, Henry Kenlay, Li Zhang +2
Graph convolutional neural networks (GCNNs) have emerged as powerful tools for analyzing graph-structured data, achieving remarkable success across diverse applications. However, t…
Local2Global: A distributed approach for scaling representation learning on graphs
Lucas G. S. Jeub, Giovanni Colavizza, Xiaowen Dong +2
We propose a decentralised "local2global"' approach to graph representation learning, that one can a-priori use to scale any embedding technique. Our local2global approach proceeds…
Local2Global: Scaling global representation learning on graphs via local training
Lucas G. S. Jeub, Giovanni Colavizza, Xiaowen Dong +2
We propose a decentralised "local2global" approach to graph representation learning, that one can a-priori use to scale any embedding technique. Our local2global approach proceeds…