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Found 13 papers, 5 papers with code
Mitigating Subpopulation Bias for Fair Network Topology Inference
We consider fair network topology inference from nodal observations.
Recovering Missing Node Features with Local Structure-based Embeddings
We present a framework to recover completely missing node features for a set of graphs, where we only know the signals of a subset of graphs.
SC-MAD: Mixtures of Higher-order Networks for Data Augmentation
The myriad complex systems with multiway interactions motivate the extension of graph-based pairwise connections to higher-order relations.
Data Augmentation via Subgroup Mixup for Improving Fairness
Furthermore, our method allows us to use the generalization ability of mixup to improve both fairness and accuracy.
Joint Network Topology Inference in the Presence of Hidden Nodes
We investigate the increasingly prominent task of jointly inferring multiple networks from nodal observations.
Joint graph learning from Gaussian observations in the presence of hidden nodes
Motivated by this, we propose a joint graph learning method that takes into account the presence of hidden (latent) variables.
GraphMAD: Graph Mixup for Data Augmentation using Data-Driven Convex Clustering
Mixup is a data augmentation method to create new training data by linearly interpolating between pairs of data samples and their labels.
Joint Network Topology Inference via a Shared Graphon Model
The proposed joint network and graphon estimation is further enhanced with the introduction of a robust method for noisy graph sampling information.
Graphon-aided Joint Estimation of Multiple Graphs
We consider the problem of estimating the topology of multiple networks from nodal observations, where these networks are assumed to be drawn from the same (unknown) random graph model.
Network Clustering for Latent State and Changepoint Detection
In this paper, we propose a convex approach for the task of network clustering.
Joint inference of multiple graphs with hidden variables from stationary graph signals
Learning graphs from sets of nodal observations represents a prominent problem formally known as graph topology inference.
Joint Inference of Multiple Graphs from Matrix Polynomials
Inferring graph structure from observations on the nodes is an important and popular network science task.
Network Topology Inference with Graphon Spectral Penalties
In particular, we consider the case where the graph was drawn from a graphon model, and we supplement our convex optimization problem with a provably-valid regularizer on the spectrum of the graph to be recovered.
