Optimal block-wise asymmetric graph construction for graph-based semi-supervised learning
Graph-based semi-supervised learning (GSSL) serves as a powerful tool to model the
underlying manifold structures of samples in high-dimensional spaces. It involves two …
underlying manifold structures of samples in high-dimensional spaces. It involves two …
Graph structure learning with interpretable Bayesian neural networks
M Wasserman, G Mateos - arXiv preprint arXiv:2406.14786, 2024 - arxiv.org
Graphs serve as generic tools to encode the underlying relational structure of data. Often
this graph is not given, and so the task of inferring it from nodal observations becomes …
this graph is not given, and so the task of inferring it from nodal observations becomes …
Enhanced graph-learning schemes driven by similar distributions of motifs
This paper looks at the task of network topology inference, where the goal is to learn an
unknown graph from nodal observations. One of the novelties of the approach put forth is the …
unknown graph from nodal observations. One of the novelties of the approach put forth is the …
Joint network topology inference in the presence of hidden nodes
We investigate the increasingly prominent task of jointly inferring multiple networks from
nodal observations. While most joint inference methods assume that observations are …
nodal observations. While most joint inference methods assume that observations are …
A dual Laplacian framework with effective graph learning for unified fair spectral clustering
X Zhang, Q Wang - Neurocomputing, 2024 - Elsevier
We consider the problem of spectral clustering under group fairness constraints, where
samples from each sensitive group are approximately proportionally represented in each …
samples from each sensitive group are approximately proportionally represented in each …
Joint graph learning and blind separation of smooth graph signals using minimization of mutual information and Laplacian quadratic forms
A Einizade, SH Sardouie - IEEE Transactions on Signal and …, 2023 - ieeexplore.ieee.org
The smoothness of graph signals has found desirable real applications for processing
irregular (graph-based) signals. When the latent sources of the mixtures provided to us as …
irregular (graph-based) signals. When the latent sources of the mixtures provided to us as …
Discovering influencers in opinion formation over social graphs
The adaptive social learning paradigm helps model how networked agents are able to form
opinions on a state of nature and track its drifts in a changing environment. In this framework …
opinions on a state of nature and track its drifts in a changing environment. In this framework …
Graph learning from incomplete graph signals: From batch to online methods
X Zhang, Q Wang - Signal Processing, 2025 - Elsevier
Inferring graph topologies from data is crucial in many graph-related applications. Existing
works typically assume that signals are observed at all nodes, which may not hold due to …
works typically assume that signals are observed at all nodes, which may not hold due to …
A unified framework for fair spectral clustering with effective graph learning
X Zhang, Q Wang - arXiv preprint arXiv:2311.13766, 2023 - arxiv.org
We consider the problem of spectral clustering under group fairness constraints, where
samples from each sensitive group are approximately proportionally represented in each …
samples from each sensitive group are approximately proportionally represented in each …
A Linearly Convergent Optimization Framework for Learning Graphs From Smooth Signals
Learning graph structures from a collection of smooth graph signals is a fundamental
problem in data analysis and has attracted much interest in recent years. Although various …
problem in data analysis and has attracted much interest in recent years. Although various …