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Directed acyclic graphs (DAGs) serve as crucial data representations in domains such as hardware synthesis and compiler/program optimization for computing systems. DAG generative models facilitate the creation of synthetic DAGs, which can…

Machine Learning · Computer Science 2025-03-04 Mufei Li , Viraj Shitole , Eli Chien , Changhai Man , Zhaodong Wang , Srinivas Sridharan , Ying Zhang , Tushar Krishna , Pan Li

Learning a faithful directed acyclic graph (DAG) from samples of a joint distribution is a challenging combinatorial problem, owing to the intractable search space superexponential in the number of graph nodes. A recent breakthrough…

Machine Learning · Computer Science 2019-04-24 Yue Yu , Jie Chen , Tian Gao , Mo Yu

Supervised learning algorithms generally assume the availability of enough memory to store data models during the training and test phases. However, this assumption is unrealistic when data comes in the form of infinite data streams, or…

Machine Learning · Computer Science 2022-10-13 Martin Khannouz , Tristan Glatard

The compact directed acyclic word graph (CDAWG) of a string $T$ is an index occupying $O(\mathsf{e})$ space, where $\mathsf{e}$ is the number of right extensions of maximal repeats in $T$. For highly repetitive datasets, the measure…

Data Structures and Algorithms · Computer Science 2024-10-22 Shunsuke Inenaga , Dmitry Kosolobov

Maximal Ancestral Graphs (MAGs) provide an abstract representation of Directed Acyclic Graphs (DAGs) with latent (selection) variables. These graphical objects encode information about ancestral relations and d-separations of the DAGs they…

Discrete Mathematics · Computer Science 2025-09-25 Binghua Yao , Joris M. Mooij

Given a string $T$, it is known that its suffix tree can be represented using the compact directed acyclic word graph (CDAWG) with $e_T$ arcs, taking overall $O(e_T+e_{{\overline{T}}})$ words of space, where ${\overline{T}}$ is the reverse…

Data Structures and Algorithms · Computer Science 2017-05-25 Djamal Belazzougui , Fabio Cunial

Compact directed acyclic word graphs (CDAWGs) [Blumer et al. 1987] are a fundamental data structure on strings with applications in text pattern searching, data compression, and pattern discovery. Intuitively, the CDAWG of a string $T$ is…

Data Structures and Algorithms · Computer Science 2025-02-11 Rikuya Hamai , Hiroto Fujimaru , Shunsuke Inenaga

The displayed tree phylogenetic network model is shown to sit as a natural submodel of the graphical model associated to a directed acyclic graph (DAG). This representation allows to derive a number of results about the displayed tree…

Populations and Evolution · Quantitative Biology 2025-08-01 Seth Sullivant

Directed acyclic graphs (DAGs) are directed graphs in which there is no path from a vertex to itself. DAGs are an omnipresent data structure in computer science and the problem of counting the DAGs of given number of vertices and to sample…

Discrete Mathematics · Computer Science 2025-10-03 Martin Pépin , Alfredo Viola

The compact directed acyclic word graph (CDAWG) [Blumer et al. 1987] of a string is the minimal compact automaton that recognizes all the suffixes of the string. CDAWGs can be used for various string tasks including text pattern searching,…

Data Structures and Algorithms · Computer Science 2026-05-29 Hiroto Fujimaru , Shunsuke Inenaga

The shrinking operation converts a hypergraph into a graph by choosing, from each hyperedge, two endvertices of a corresponding graph edge. A hypertree is a hypergraph which can be shrunk to a tree on the same vertex set. Klimo\v{s}ov\'{a}…

Combinatorics · Mathematics 2025-12-09 Karolína Hylasová , Tomáš Kaiser

We present a novel form of Fourier analysis, and associated signal processing concepts, for signals (or data) indexed by edge-weighted directed acyclic graphs (DAGs). This means that our Fourier basis yields an eigendecomposition of a…

Signal Processing · Electrical Eng. & Systems 2025-01-29 Bastian Seifert , Chris Wendler , Markus Püschel

Bayesian phylogenetics typically estimates a posterior distribution, or aspects thereof, using Markov chain Monte Carlo methods. These methods integrate over tree space by applying local rearrangements to move a tree through its space as a…

Directed acyclic graphical models, or DAG models, are widely used to represent complex causal systems. Since the basic task of learning such a model from data is NP-hard, a standard approach is greedy search over the space of directed…

Statistics Theory · Mathematics 2021-06-09 Liam Solus , Yuhao Wang , Caroline Uhler

Scientific workflows are often represented as directed acyclic graphs (DAGs), where vertices correspond to tasks and edges represent the dependencies between them. Since these graphs are often large in both the number of tasks and their…

Distributed, Parallel, and Cluster Computing · Computer Science 2024-07-15 Svetlana Kulagina , Henning Meyerhenke , Anne Benoit

Directed acyclic graph (DAG) models are popular for capturing causal relationships. From observational and interventional data, a DAG model can only be determined up to its \emph{interventional Markov equivalence class} (I-MEC). We…

Machine Learning · Statistics 2019-03-07 Dmitriy Katz , Karthikeyan Shanmugam , Chandler Squires , Caroline Uhler

When designing new molecules with particular properties, it is not only important what to make but crucially how to make it. These instructions form a synthesis directed acyclic graph (DAG), describing how a large vocabulary of simple…

Machine Learning · Computer Science 2020-12-22 John Bradshaw , Brooks Paige , Matt J. Kusner , Marwin H. S. Segler , José Miguel Hernández-Lobato

In this paper, we prove almost tight bounds on the size of sound monotone switching networks accepting permutations sets of directed trees. This roughly corresponds to proving almost tight bounds bounds on the monotone memory efficiency of…

Discrete Mathematics · Computer Science 2013-01-17 Joshua Brakensiek , Aaron Potechin

Assuming a directed acyclic graph (DAG) that represents prior knowledge of causal relationships between variables is a common starting point for cause-effect estimation. Existing literature typically invokes hypothetical domain expert…

Machine Learning · Statistics 2025-03-11 Kirtan Padh , Zhufeng Li , Cecilia Casolo , Niki Kilbertus

THIS PAPER IS NOW DEFUNCT: Check out "Graph Embeddings via Tensor Products and Approximately Orthonormal Codes", where it has been combined into one paper.

Machine Learning · Statistics 2023-05-16 Frank Qiu