ExMAG: Learning of Maximally Ancestral Graphs

• URL: http://arxiv.org/abs/2503.08245v2
• Date: Tue, 01 Apr 2025 10:35:52 GMT
• Title: ExMAG: Learning of Maximally Ancestral Graphs
• Authors: Petr Ryšavý, Pavel Rytíř, Xiaoyu He, Georgios Korpas, Jakub Mareček,
• Abstract summary: We propose a score-based learning algorithm for learning maximally ancestral graphs.<n>We show that the proposed approach turns out to produce more accurate results when applied to small and medium-sized synthetic instances up to 25 variables.
• Score: 2.740961764574832
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: As one transitions from statistical to causal learning, one is seeking the most appropriate causal model. Dynamic Bayesian networks are a popular model, where a weighted directed acyclic graph represents the causal relationships. Stochastic processes are represented by its vertices, and weighted oriented edges suggest the strength of the causal relationships. When there are confounders, one would like to utilize both oriented edges (when the direction of causality is clear) and edges that are not oriented (when there is a confounder or not a relationship), yielding mixed graphs. A little-studied extension of acyclicity to this mixed-graph setting is known as maximally ancestral graphs with consideration of confounders. We propose a score-based learning algorithm for learning maximally ancestral graphs. A mixed-integer quadratic program is formulated, and an algorithmic approach is proposed, in which the pre-generation of exponentially many constraints is avoided by generating only violated constraints in the so-called branch-and-cut (``lazy constraint'') method. Comparing the novel approach to the state-of-the-art, we show that the proposed approach turns out to produce more accurate results when applied to small and medium-sized synthetic instances containing up to 25 variables.

Related papers

• ExDBN: Exact learning of Dynamic Bayesian Networks [2.2499166814992435]
  We propose a score-based learning approach for causal learning from data. We show that the proposed approach turns out to produce excellent results when applied to small and medium-sized synthetic instances of up to 25 time-series. Two interesting applications in bio-science and finance, to which the method is directly applied, further stress the opportunities in developing highly accurate, globally convergent solvers.
  arXiv  Detail & Related papers  (2024-10-21T15:27:18Z)
• Sparse Training of Discrete Diffusion Models for Graph Generation [45.103518022696996]
  We introduce SparseDiff, a novel diffusion model based on the observation that almost all large graphs are sparse. By selecting a subset of edges, SparseDiff effectively leverages sparse graph representations both during the noising process and within the denoising network. Our model demonstrates state-of-the-art performance across multiple metrics on both small and large datasets.
  arXiv  Detail & Related papers  (2023-11-03T16:50:26Z)
• 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)
• Latent Random Steps as Relaxations of Max-Cut, Min-Cut, and More [30.919536115917726]
  We present a probabilistic model based on non-negative matrix factorization which unifies clustering and simplification. By relaxing the hard clustering to a soft clustering, our algorithm relaxes potentially hard clustering problems to a tractable ones.
  arXiv  Detail & Related papers  (2023-08-12T02:47:57Z)
• Optimal Propagation for Graph Neural Networks [51.08426265813481]
  We propose a bi-level optimization approach for learning the optimal graph structure. We also explore a low-rank approximation model for further reducing the time complexity.
  arXiv  Detail & Related papers  (2022-05-06T03:37:00Z)
• Explanation Graph Generation via Pre-trained Language Models: An Empirical Study with Contrastive Learning [84.35102534158621]
  We study pre-trained language models that generate explanation graphs in an end-to-end manner. We propose simple yet effective ways of graph perturbations via node and edge edit operations. Our methods lead to significant improvements in both structural and semantic accuracy of explanation graphs.
  arXiv  Detail & Related papers  (2022-04-11T00:58:27Z)
• Bayesian Graph Contrastive Learning [55.36652660268726]
  We propose a novel perspective of graph contrastive learning methods showing random augmentations leads to encoders. Our proposed method represents each node by a distribution in the latent space in contrast to existing techniques which embed each node to a deterministic vector. We show a considerable improvement in performance compared to existing state-of-the-art methods on several benchmark datasets.
  arXiv  Detail & Related papers  (2021-12-15T01:45:32Z)
• 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)
• Learning Graphs from Smooth Signals under Moment Uncertainty [23.868075779606425]
  We consider the problem of inferring the graph structure from a given set of graph signals. Traditional graph learning models do not take this distributional uncertainty into account.
  arXiv  Detail & Related papers  (2021-05-12T06:47:34Z)
• Deep Graph Matching under Quadratic Constraint [30.72996618021077]
  We propose to explicitly formulate pairwise graph structures as a textbfquadratic constraint incorporated into the deep graph matching framework. The quadratic constraint minimizes the pairwise structural discrepancy between graphs, which can reduce the ambiguities brought by only using the extracted CNN features. To give more precise and proper supervision, a well-designed false matching loss against class imbalance is proposed, which can better penalize the false negatives and false positives with less overfitting.
  arXiv  Detail & Related papers  (2021-03-11T12:51:12Z)
• A Thorough View of Exact Inference in Graphs from the Degree-4 Sum-of-Squares Hierarchy [37.34153902687548]
  We tackle the problem of exactly recovering an unknown ground-truth binary labeling of the nodes from a single corrupted observation of each edge. We apply a hierarchy of relaxations known as the sum-of-squares hierarchy, to the problem. We show that the solution of the dual of the relaxed problem is related to finding edge weights of the Johnson and Kneser graphs.
  arXiv  Detail & Related papers  (2021-02-16T08:36:19Z)
• 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)
• Online Dense Subgraph Discovery via Blurred-Graph Feedback [87.9850024070244]
  We introduce a novel learning problem for dense subgraph discovery. We first propose a edge-time algorithm that obtains a nearly-optimal solution with high probability. We then design a more scalable algorithm with a theoretical guarantee.
  arXiv  Detail & Related papers  (2020-06-24T11:37:33Z)

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.