12 citations · 18 across the 5 of their papers we have counts for
5 papers
NervePool: A Simplicial Pooling Layer
Sarah McGuire Scullen, Ernst Röell, Elizabeth Munch +2
For deep learning problems on graph-structured data, pooling layers are important for down sampling, reducing computational cost, and to minimize overfitting. We define a pooling l…
Do Neural Networks Trained with Topological Features Learn Different Internal Representations?
Sarah McGuire, Shane Jackson, Tegan Emerson +1
There is a growing body of work that leverages features extracted via topological data analysis to train machine learning models. While this field, sometimes known as topological m…
Taxonomy of Benchmarks in Graph Representation Learning
Renming Liu, Semih Cantürk, Frederik Wenkel +9
Graph Neural Networks (GNNs) extend the success of neural networks to graph-structured data by accounting for their intrinsic geometry. While extensive research has been done on de…
Towards a Taxonomy of Graph Learning Datasets
Renming Liu, Semih Cantürk, Frederik Wenkel +10
Graph neural networks (GNNs) have attracted much attention due to their ability to leverage the intrinsic geometries of the underlying data. Although many different types of GNN mo…
Multi-Temporal Analysis and Scaling Relations of 100,000,000,000 Network Packets
Jeremy Kepner, Chad Meiners, Chansup Byun +23
Our society has never been more dependent on computer networks. Effective utilization of networks requires a detailed understanding of the normal background behaviors of network tr…