You can also access our individual websites (via the Members page) for further information about our research and lists of our publications.
@article{Peach:2020:10.3934/fods.2020002,
author = {Peach, RL and Arnaudon, A and Barahona, M},
doi = {10.3934/fods.2020002},
journal = {Foundations of Data Science},
pages = {19--33},
title = {Semi-supervised classification on graphs using explicit diffusion dynamics},
url = {http://dx.doi.org/10.3934/fods.2020002},
volume = {2},
year = {2020}
}
TY - JOUR
AB - Classification tasks based on feature vectors can be significantly improved by including within deep learning a graph that summarises pairwise relationships between the samples. Intuitively, the graph acts as a conduit to channel and bias the inference of class labels. Here, we study classification methods that consider the graph as the originator of an explicit graph diffusion. We show that appending graph diffusion to feature-based learning as a posteriori refinement achieves state-of-the-art classification accuracy. This method, which we call Graph Diffusion Reclassification (GDR), uses overshooting events of a diffusive graph dynamics to reclassify individual nodes. The method uses intrinsic measures of node influence, which are distinct for each node, and allows the evaluation of the relationship and importance of features and graph for classification. We also present diff-GCN, a simple extension of Graph Convolutional Neural Network (GCN) architectures that leverages explicit diffusion dynamics, and allows the natural use of directed graphs. To showcase our methods, we use benchmark datasets of documents with associated citation data.
AU - Peach,RL
AU - Arnaudon,A
AU - Barahona,M
DO - 10.3934/fods.2020002
EP - 33
PY - 2020///
SN - 2639-8001
SP - 19
TI - Semi-supervised classification on graphs using explicit diffusion dynamics
T2 - Foundations of Data Science
UR - http://dx.doi.org/10.3934/fods.2020002
UR - http://hdl.handle.net/10044/1/75893
VL - 2
ER -