4 citations · 4 across the 7 of their papers we have counts for
5 papers · 1 filter
Graph Neural Networks Are Not Continuous Across Graph Resolutions
Christian Koke, Yuesong Shen, Abhishek Saroha +4
We show that contrary to conventional wisdom in the community, graph neural networks (GNNs) are not continuous with respect to all natural modes of graph convergence. As a result,…
HoloNets: Spectral Convolutions do extend to Directed Graphs
Christian Koke, Daniel Cremers
Within the graph learning community, conventional wisdom dictates that spectral convolutional networks may only be deployed on undirected graphs: Only there could the existence of…
ResolvNet: A Graph Convolutional Network with multi-scale Consistency
Christian Koke, Abhishek Saroha, Yuesong Shen +2
It is by now a well known fact in the graph learning community that the presence of bottlenecks severely limits the ability of graph neural networks to propagate information over l…
Graph Scattering beyond Wavelet Shackles
Christian Koke, Gitta Kutyniok
This work develops a flexible and mathematically sound framework for the design and analysis of graph scattering networks with variable branching ratios and generic functional calc…
Limitless stability for Graph Convolutional Networks
Christian Koke
This work establishes rigorous, novel and widely applicable stability guarantees and transferability bounds for graph convolutional networks -- without reference to any underlying…