paper-with-me

홈 › Papers

Stable mixed graphs

2011-10-19 · Kayvan Sadeghi

In this paper, we study classes of graphs with three types of edges that capture the modified independence structure of a directed acyclic graph (DAG) after marginalisation over unobserved variables and conditioning on selection variables using the $m$-separation criterion. These include MC, summary, and ancestral graphs. As a modification of MC graphs, we define the class of ribbonless graphs (RGs) that permits the use of the $m$-separation criterion. RGs contain summary and ancestral graphs as subclasses, and each RG can be generated by a DAG after marginalisation and conditioning. We derive simple algorithms to generate RGs, from given DAGs or RGs, and also to generate summary and ancestral graphs in a simple way by further extension of the RG-generating algorithm. This enables us to develop a parallel theory on these three classes and to study the relationships between them as well as the use of each class.

📄 PDF Abstract BibTeX arXiv:1110.4168

Code (0)

등록된 구현이 없습니다.

Similar Papers 제목 키워드 기반

Marginalization and Conditioning for LWF Chain Graphs

2014-05-28 · Kayvan Sadeghi

In this paper, we deal with the problem of marginalization over and conditioning on two disjoint subsets of the node set of chain graphs (CGs) with the LWF Markov property. For this purpose, we define the class of chain …

Stable Blanket with Hidden Variables and Cycles

2026-05-03 · Hanqing Xiang arxiv

Stabilized regression aims to identify a set of predictors whose conditional relationship with a response variable remains invariant across different environments. Existing graphical characterizations of the stable blank…

Unified Multi-Domain Graph Pre-training for Homogeneous and Heterogeneous Graphs via Domain-Specific Expert Encoding

2026-02-13 · Chundong Liang, Yongqi Huang, Dongxiao He, Peiyuan Li 외 arxiv

Graph pre-training has achieved remarkable success in recent years, delivering transferable representations for downstream adaptation. However, most existing methods are designed for either homogeneous or heterogeneous g…

Product Manifold Representations for Learning on Biological Pathways

2024-01-27 · Daniel McNeela, Frederic Sala, Anthony Gitter

Machine learning models that embed graphs in non-Euclidean spaces have shown substantial benefits in a variety of contexts, but their application has not been studied extensively in the biological domain, particularly wi…

Graph Neural NetworkGraph Representation LearningRepresentation Learning

Markov properties for mixed graphs

2011-09-27 · Kayvan Sadeghi, Steffen Lauritzen

In this paper, we unify the Markov theory of a variety of different types of graphs used in graphical Markov models by introducing the class of loopless mixed graphs, and show that all independence models induced by $m$-…