Informed Greedy Algorithm for Scalable Bayesian Network Fusion via Minimum Cut Analysis

• URL: http://arxiv.org/abs/2504.00467v1
• Date: Tue, 01 Apr 2025 06:47:33 GMT
• Title: Informed Greedy Algorithm for Scalable Bayesian Network Fusion via Minimum Cut Analysis
• Authors: Pablo Torrijos, José M. Puerta, José A. Gámez, Juan A. Aledo,
• Abstract summary: This paper presents the Greedy Min-Cut Bayesian Consensus (GMCBC) algorithm for the structural fusion of Bayesian Networks (BNs)<n>The method is designed to preserve essential dependencies while controlling network complexity.<n>It addresses the limitations of traditional fusion approaches, which often lead to excessively complex models.
• Score: 1.7086867242274812
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: This paper presents the Greedy Min-Cut Bayesian Consensus (GMCBC) algorithm for the structural fusion of Bayesian Networks (BNs). The method is designed to preserve essential dependencies while controlling network complexity. It addresses the limitations of traditional fusion approaches, which often lead to excessively complex models that are impractical for inference, reasoning, or real-world applications. As the number and size of input networks increase, this issue becomes even more pronounced. GMCBC integrates principles from flow network theory into BN fusion, adapting the Backward Equivalence Search (BES) phase of the Greedy Equivalence Search (GES) algorithm and applying the Ford-Fulkerson algorithm for minimum cut analysis. This approach removes non-essential edges, ensuring that the fused network retains key dependencies while minimizing unnecessary complexity. Experimental results on synthetic Bayesian Networks demonstrate that GMCBC achieves near-optimal network structures. In federated learning simulations, GMCBC produces a consensus network that improves structural accuracy and dependency preservation compared to the average of the input networks, resulting in a structure that better captures the real underlying (in)dependence relationships. This consensus network also maintains a similar size to the original networks, unlike unrestricted fusion methods, where network size grows exponentially.

Related papers

• SGLP: A Similarity Guided Fast Layer Partition Pruning for Compressing Large Deep Models [19.479746878680707]
  Layer pruning is a potent approach to reduce network size and improve computational efficiency. We propose a Similarity Guided fast Layer Partition pruning for compressing large deep models. Our method outperforms the state-of-the-art methods in both accuracy and computational efficiency.
  arXiv  Detail & Related papers  (2024-10-14T04:01:08Z)
• Node Centrality Approximation For Large Networks Based On Inductive Graph Neural Networks [2.4012886591705738]
  Closeness Centrality (CC) and Betweenness Centrality (BC) are crucial metrics in network analysis. Their practical implementation on extensive networks remains computationally demanding due to their high time complexity. We propose the CNCA-IGE model, which is an inductive graph encoder-decoder model designed to rank nodes based on specified CC or BC metrics.
  arXiv  Detail & Related papers  (2024-03-08T01:23:12Z)
• Hierarchical Multi-Marginal Optimal Transport for Network Alignment [52.206006379563306]
  Multi-network alignment is an essential prerequisite for joint learning on multiple networks. We propose a hierarchical multi-marginal optimal transport framework named HOT for multi-network alignment. Our proposed HOT achieves significant improvements over the state-of-the-art in both effectiveness and scalability.
  arXiv  Detail & Related papers  (2023-10-06T02:35:35Z)
• DANI: Fast Diffusion Aware Network Inference with Preserving Topological Structure Property [2.8948274245812327]
  We propose a novel method called DANI to infer the underlying network while preserving its structural properties. DANI has higher accuracy and lower run time while maintaining structural properties, including modular structure, degree distribution, connected components, density, and clustering coefficients.
  arXiv  Detail & Related papers  (2023-10-02T23:23:00Z)
• Optimization Guarantees of Unfolded ISTA and ADMM Networks With Smooth Soft-Thresholding [57.71603937699949]
  We study optimization guarantees, i.e., achieving near-zero training loss with the increase in the number of learning epochs. We show that the threshold on the number of training samples increases with the increase in the network width.
  arXiv  Detail & Related papers  (2023-09-12T13:03:47Z)
• Algorithm Unrolling for Massive Access via Deep Neural Network with Theoretical Guarantee [30.86806523281873]
  Massive access is a critical design challenge of Internet of Things (IoT) networks. We consider the grant-free uplink transmission of an IoT network with a multiple-antenna base station (BS) and a large number of single-antenna IoT devices. We propose a novel algorithm unrolling framework based on the deep neural network to simultaneously achieve low computational complexity and high robustness.
  arXiv  Detail & Related papers  (2021-06-19T05:23:05Z)
• Manifold Regularized Dynamic Network Pruning [102.24146031250034]
  This paper proposes a new paradigm that dynamically removes redundant filters by embedding the manifold information of all instances into the space of pruned networks. The effectiveness of the proposed method is verified on several benchmarks, which shows better performance in terms of both accuracy and computational cost.
  arXiv  Detail & Related papers  (2021-03-10T03:59:03Z)
• Distributed Optimization, Averaging via ADMM, and Network Topology [0.0]
  We study the connection between network topology and convergence rates for different algorithms on a real world problem of sensor localization. We also show interesting connections between ADMM and lifted Markov chains besides providing an explicitly characterization of its convergence.
  arXiv  Detail & Related papers  (2020-09-05T21:44:39Z)
• Fitting the Search Space of Weight-sharing NAS with Graph Convolutional Networks [100.14670789581811]
  We train a graph convolutional network to fit the performance of sampled sub-networks. With this strategy, we achieve a higher rank correlation coefficient in the selected set of candidates.
  arXiv  Detail & Related papers  (2020-04-17T19:12:39Z)
• Network Adjustment: Channel Search Guided by FLOPs Utilization Ratio [101.84651388520584]
  This paper presents a new framework named network adjustment, which considers network accuracy as a function of FLOPs. Experiments on standard image classification datasets and a wide range of base networks demonstrate the effectiveness of our approach.
  arXiv  Detail & Related papers  (2020-04-06T15:51:00Z)
• 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.