Genetic Algorithms for Tractable Bayesian Network Fusion via Pre-Fusion Edge Pruning

• URL: http://arxiv.org/abs/2609.03724v1
• Date: Thu, 03 Sep 2026 11:55:48 GMT
• Title: Genetic Algorithms for Tractable Bayesian Network Fusion via Pre-Fusion Edge Pruning
• Abstract summary: This paper introduces a consensus framework that prioritizes shared structures among input networks while enforcing treewidth constraints.<n> Experiments on synthetic and real-world BNs show the superiority of the proposed genetic algorithms over the adapted methods and greedy baselines.
• Score: 3.5232028627328895
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: Bayesian Network (BN) fusion combines multiple input networks into a single structure, balancing dependency preservation with computational tractability. While unrestricted fusion retains all dependencies, it often results in overly complex networks with high treewidth, which affects inference scalability. Limited fusion mitigates this by pruning edges to control treewidth but risks overfitting to input-specific noise and omitting dependencies from the original BNs. This paper introduces a consensus framework that prioritizes shared structures among input networks while enforcing treewidth constraints, ensuring a good consensus. We propose genetic algorithms with advanced initialization, specialized operators, and a tailored fitness function. Additionally, we adapt existing methods to this problem and implement greedy baselines for benchmarking and further optimization. Experiments on synthetic and real-world BNs show the superiority of the proposed genetic algorithms over the adapted methods and greedy baselines.

Related papers

• Structural Fusion of Bayesian Networks with Limited Treewidth Using Genetic Algorithms [3.4473935399022415]
  This paper introduces an evolutionary approach for consensus in structural Bayesian Network (BN) fusion under the constraint of limited treewidth.<n>The consensus BN aims to reconcile multiple input BNs into a single one that retains key structural features present in the original networks.<n>A genetic algorithm is proposed to look for a BN that codifies as much information about the unrestricted fusion as possible while ensuring the treewidth restriction.
  arXiv  Detail & Related papers  (2026-09-09T14:57:11Z)
• Variational Bayesian Flow Network for Graph Generation [54.94088904387278]
  We propose Variational Bayesian Flow Network (VBFN) for graph generation.<n>VBFN performs variational lifting to a tractable joint Gaussian variational belief family governed by structured precisions.<n>On synthetic and molecular graph datasets, VBFN improves fidelity and diversity, and surpasses baseline methods.
  arXiv  Detail & Related papers  (2026-01-30T03:59:38Z)
• A Federated Fine-Tuning Paradigm of Foundation Models in Heterogenous Wireless Networks [43.049216349313575]
  Edge devices switch to LoRA modules dynamically for federated fine-tuning with base station to jointly mitigate the impact of heterogeneous transmission.<n>An online optimization algorithm is developed to solve problems with computational complexity.<n>The simulation results on the SST-2 and QNLI data sets demonstrate the performance gains in test accuracy and energy efficiency.
  arXiv  Detail & Related papers  (2025-09-05T06:38:36Z)
• Bayesian Network Structural Consensus via Greedy Min-Cut Analysis [3.5232028627328895]
  Min-Cut Bayesian Network Consensus (MCBNC) is a greedy method for structural consensus of Bayesian Networks (BNs)<n>MCBNC prunes weak edges from an initial unrestricted fusion using a structural score based on min-cut analysis.<n>The method is scalable, data-agnostic, and well-suited for distributed or federated scenarios.
  arXiv  Detail & Related papers  (2025-04-01T06:47:33Z)
• Robust Training and Verification of Implicit Neural Networks: A Non-Euclidean Contractive Approach [64.23331120621118]
  This paper proposes a theoretical and computational framework for training and robustness verification of implicit neural networks. We introduce a related embedded network and show that the embedded network can be used to provide an $ell_infty$-norm box over-approximation of the reachable sets of the original network. We apply our algorithms to train implicit neural networks on the MNIST dataset and compare the robustness of our models with the models trained via existing approaches in the literature.
  arXiv  Detail & Related papers  (2022-08-08T03:13:24Z)
• Edge Rewiring Goes Neural: Boosting Network Resilience via Policy Gradient [62.660451283548724]
  ResiNet is a reinforcement learning framework to discover resilient network topologies against various disasters and attacks. We show that ResiNet achieves a near-optimal resilience gain on multiple graphs while balancing the utility, with a large margin compared to existing approaches.
  arXiv  Detail & Related papers  (2021-10-18T06:14:28Z)
• Layer Adaptive Node Selection in Bayesian Neural Networks: Statistical Guarantees and Implementation Details [0.5156484100374059]
  Sparse deep neural networks have proven to be efficient for predictive model building in large-scale studies. We propose a Bayesian sparse solution using spike-and-slab Gaussian priors to allow for node selection during training. We establish the fundamental result of variational posterior consistency together with the characterization of prior parameters.
  arXiv  Detail & Related papers  (2021-08-25T00:48:07Z)
• Improved Branch and Bound for Neural Network Verification via Lagrangian Decomposition [161.09660864941603]
  We improve the scalability of Branch and Bound (BaB) algorithms for formally proving input-output properties of neural networks. We present a novel activation-based branching strategy and a BaB framework, named Branch and Dual Network Bound (BaDNB) BaDNB outperforms previous complete verification systems by a large margin, cutting average verification times by factors up to 50 on adversarial properties.
  arXiv  Detail & Related papers  (2021-04-14T09:22:42Z)
• On Topology Optimization and Routing in Integrated Access and Backhaul Networks: A Genetic Algorithm-based Approach [70.85399600288737]
  We study the problem of topology optimization and routing in IAB networks. We develop efficient genetic algorithm-based schemes for both IAB node placement and non-IAB backhaul link distribution. We discuss the main challenges for enabling mesh-based IAB networks.
  arXiv  Detail & Related papers  (2021-02-14T21:52:05Z)
• MSE-Optimal Neural Network Initialization via Layer Fusion [68.72356718879428]
  Deep neural networks achieve state-of-the-art performance for a range of classification and inference tasks. The use of gradient combined nonvolutionity renders learning susceptible to novel problems. We propose fusing neighboring layers of deeper networks that are trained with random variables.
  arXiv  Detail & Related papers  (2020-01-28T18:25:15Z)

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.