research
∙
09/16/2023
Recovering Missing Node Features with Local Structure-based Embeddings
Node features bolster graph-based learning when exploited jointly with n...
share
research
∙
09/14/2023
SC-MAD: Mixtures of Higher-order Networks for Data Augmentation
The myriad complex systems with multiway interactions motivate the exten...
research
∙
09/13/2023
Data Augmentation via Subgroup Mixup for Improving Fairness
In this work, we propose data augmentation via pairwise mixup across sub...
research
∙
12/04/2022
Joint graph learning from Gaussian observations in the presence of hidden nodes
Graph learning problems are typically approached by focusing on learning...
research
∙
10/27/2022
GraphMAD: Graph Mixup for Data Augmentation using Data-Driven Convex Clustering
We develop a novel data-driven nonlinear mixup mechanism for graph data ...
research
∙
09/17/2022
Joint Network Topology Inference via a Shared Graphon Model
We consider the problem of estimating the topology of multiple networks ...
research
∙
02/11/2022
Graphon-aided Joint Estimation of Multiple Graphs
We consider the problem of estimating the topology of multiple networks ...
research
∙
11/01/2021
Network Clustering for Latent State and Changepoint Detection
Network models provide a powerful and flexible framework for analyzing a...
research
∙
10/05/2021
Joint inference of multiple graphs with hidden variables from stationary graph signals
Learning graphs from sets of nodal observations represents a prominent p...
research
∙
10/16/2020