Fair Community Detection and Structure Learning in Heterogeneous Graphical Models

• URL: http://arxiv.org/abs/2112.05128v2
• Date: Thu, 30 Nov 2023 06:04:52 GMT
• Title: Fair Community Detection and Structure Learning in Heterogeneous Graphical Models
• Authors: Davoud Ataee Tarzanagh, Laura Balzano, and Alfred O. Hero
• Abstract summary: Inference of community structure in probabilistic graphical models may not be consistent with fairness constraints when nodes have demographic attributes. This paper defines a novel $ell_$-regularized pseudo-likelihood approach for fair graphical model selection.
• Score: 8.643517734716607
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: Inference of community structure in probabilistic graphical models may not be consistent with fairness constraints when nodes have demographic attributes. Certain demographics may be over-represented in some detected communities and under-represented in others. This paper defines a novel $\ell_1$-regularized pseudo-likelihood approach for fair graphical model selection. In particular, we assume there is some community or clustering structure in the true underlying graph, and we seek to learn a sparse undirected graph and its communities from the data such that demographic groups are fairly represented within the communities. In the case when the graph is known a priori, we provide a convex semidefinite programming approach for fair community detection. We establish the statistical consistency of the proposed method for both a Gaussian graphical model and an Ising model for, respectively, continuous and binary data, proving that our method can recover the graphs and their fair communities with high probability.

Related papers

• M3C: A Framework towards Convergent, Flexible, and Unsupervised Learning of Mixture Graph Matching and Clustering [57.947071423091415]
  We introduce Minorize-Maximization Matching and Clustering (M3C), a learning-free algorithm that guarantees theoretical convergence. We develop UM3C, an unsupervised model that incorporates novel edge-wise affinity learning and pseudo label selection. Our method outperforms state-of-the-art graph matching and mixture graph matching and clustering approaches in both accuracy and efficiency.
  arXiv  Detail & Related papers  (2023-10-27T19:40:34Z)
• Curvature-based Clustering on Graphs [14.136746708037402]
  We study algorithms, which exploit the geometry of the graph to identify densely connected substructures, which form clusters or communities. Our method implements discrete Ricci curvatures and their associated geometric flows, under which the edge weights of the graph evolve to reveal its community structure.
  arXiv  Detail & Related papers  (2023-07-19T17:35:08Z)
• FairGen: Towards Fair Graph Generation [76.34239875010381]
  We propose a fairness-aware graph generative model named FairGen. Our model jointly trains a label-informed graph generation module and a fair representation learning module. Experimental results on seven real-world data sets, including web-based graphs, demonstrate that FairGen obtains performance on par with state-of-the-art graph generative models.
  arXiv  Detail & Related papers  (2023-03-30T23:30:42Z)
• Graph-in-Graph (GiG): Learning interpretable latent graphs in non-Euclidean domain for biological and healthcare applications [52.65389473899139]
  Graphs are a powerful tool for representing and analyzing unstructured, non-Euclidean data ubiquitous in the healthcare domain. Recent works have shown that considering relationships between input data samples have a positive regularizing effect for the downstream task. We propose Graph-in-Graph (GiG), a neural network architecture for protein classification and brain imaging applications.
  arXiv  Detail & Related papers  (2022-04-01T10:01:37Z)
• Exact Community Recovery in Correlated Stochastic Block Models [6.746400031322727]
  We study the problem of learning latent community structure from multiple correlated networks. Our main result derives the precise information-theoretic threshold for exact community recovery using multiple correlated graphs. We develop a novel algorithm that carefully synthesizes algorithms from the community recovery and graph matching literatures.
  arXiv  Detail & Related papers  (2022-03-29T16:44:38Z)
• Neighborhood Random Walk Graph Sampling for Regularized Bayesian Graph Convolutional Neural Networks [0.6236890292833384]
  In this paper, we propose a novel algorithm called Bayesian Graph Convolutional Network using Neighborhood Random Walk Sampling (BGCN-NRWS) BGCN-NRWS uses a Markov Chain Monte Carlo (MCMC) based graph sampling algorithm utilizing graph structure, reduces overfitting by using a variational inference layer, and yields consistently competitive classification results compared to the state-of-the-art in semi-supervised node classification.
  arXiv  Detail & Related papers  (2021-12-14T20:58:27Z)
• A Robust and Generalized Framework for Adversarial Graph Embedding [73.37228022428663]
  We propose a robust framework for adversarial graph embedding, named AGE. AGE generates the fake neighbor nodes as the enhanced negative samples from the implicit distribution. Based on this framework, we propose three models to handle three types of graph data.
  arXiv  Detail & Related papers  (2021-05-22T07:05:48Z)
• The KL-Divergence between a Graph Model and its Fair I-Projection as a Fairness Regularizer [17.660861923996016]
  We propose a generic approach applicable to most probabilistic graph modeling approaches. Specifically, we first define the class of fair graph models corresponding to a chosen set of fairness criteria. We demonstrate that using this fairness regularizer in combination with existing graph modeling approaches efficiently trades-off fairness with accuracy.
  arXiv  Detail & Related papers  (2021-03-02T16:26:37Z)
• Amortized Probabilistic Detection of Communities in Graphs [39.56798207634738]
  We propose a simple framework for amortized community detection. We combine the expressive power of GNNs with recent methods for amortized clustering. We evaluate several models from our framework on synthetic and real datasets.
  arXiv  Detail & Related papers  (2020-10-29T16:18:48Z)
• Multilayer Clustered Graph Learning [66.94201299553336]
  We use contrastive loss as a data fidelity term, in order to properly aggregate the observed layers into a representative graph. Experiments show that our method leads to a clustered clusters w.r.t. We learn a clustering algorithm for solving clustering problems.
  arXiv  Detail & Related papers  (2020-10-29T09:58:02Z)
• Adversarial Attack on Community Detection by Hiding Individuals [68.76889102470203]
  We focus on black-box attack and aim to hide targeted individuals from the detection of deep graph community detection models. We propose an iterative learning framework that takes turns to update two modules: one working as the constrained graph generator and the other as the surrogate community detection model.
  arXiv  Detail & Related papers  (2020-01-22T09:50:04Z)

This list is automatically generated from the titles and abstracts of the papers in this site.

This site does not guarantee the quality of this site (including all information) and is not responsible for any consequences.