Efficient coding of labeled directed acyclic graphs

2003 ◽  
Vol 7 (5) ◽  
pp. 350-356 ◽  
Author(s):  
B. Steinsky
Keyword(s):  
Directed Acyclic Graphs ◽  
Efficient Coding ◽  
Acyclic Graphs

Weighted Independent Directed Acyclic Graphs for Resilient and Efficient Multipath Routing

2013 ◽  
Vol 3 (3) ◽  
pp. 1
Author(s):  
M. Ramakrishnan ◽  
Sowmya Swaminathan ◽  
Jayaganesan Chandresekaran
Keyword(s):  
Multipath Routing ◽  
Directed Acyclic Graphs ◽  
Acyclic Graphs

A Nettree for Simple Paths with Length Constraint and the Longest Path in Directed Acyclic Graphs

2012 ◽  
Vol 35 (10) ◽  
pp. 2194 ◽  
Author(s):  
Yan LI ◽  
Le SUN ◽  
Huai-Zhong ZHU ◽  
You-Xi WU
Keyword(s):  
Directed Acyclic Graphs ◽  
Length Constraint ◽  
Acyclic Graphs ◽  
Longest Path ◽  
Simple Paths

scholarly journals Directed acyclic graphs: An under-utilized tool for child maltreatment research

2019 ◽  
Vol 91 ◽  
pp. 78-87 ◽  
Author(s):  
Anna E. Austin ◽  
Tania A. Desrosiers ◽  
Meghan E. Shanahan
Keyword(s):  
Child Maltreatment ◽  
Directed Acyclic Graphs ◽  
Acyclic Graphs

scholarly journals On directed analogues of expander and hyperfinite graph sequences

2021 ◽  
pp. 1-14
Author(s):  
Endre Csóka ◽  
Łukasz Grabowski
Keyword(s):  
Directed Acyclic Graphs ◽  
Acyclic Graphs ◽  
Probabilistic Construction

Abstract We introduce and study analogues of expander and hyperfinite graph sequences in the context of directed acyclic graphs, which we call ‘extender’ and ‘hypershallow’ graph sequences, respectively. Our main result is a probabilistic construction of non-hypershallow graph sequences.

PATTERN MATCHING CODE MINIMIZATION IN REWRITING-BASED PROGRAMMING LANGUAGES

2002 ◽  
Vol 13 (06) ◽  
pp. 873-887
Author(s):  
NADIA NEDJAH ◽  
LUIZA DE MACEDO MOURELLE
Keyword(s):  
Programming Languages ◽  
Pattern Matching ◽  
Term Rewriting ◽  
Directed Acyclic Graphs ◽  
Tree Automaton ◽  
Minimal Tree ◽  
Term Rewriting Systems ◽  
Rewriting Systems ◽  
Acyclic Graphs ◽  
Space Requirements

We compile pattern matching for overlapping patterns in term rewriting systems into a minimal, tree matching automata. The use of directed acyclic graphs that shares all the isomorphic subautomata allows us to reduce space requirements. These are duplicated in the tree automaton. We design an efficient method to identify such subautomata and avoid duplicating their construction while generating the dag automaton. We compute some bounds on the size of the automata, thereby improving on previously known equivalent bounds for the tree automaton.

scholarly journals Learning Bayesian Network Classifiers: Searching in a Space of Partially Directed Acyclic Graphs

2005 ◽  
Vol 59 (3) ◽  
pp. 213-235 ◽  
Author(s):  
Silvia Acid ◽  
Luis M. de Campos ◽  
Javier G. Castellano
Keyword(s):  
Bayesian Network ◽  
Directed Acyclic Graphs ◽  
Bayesian Network Classifiers ◽  
Acyclic Graphs

scholarly journals Response to Letter to the Editor, “Directed Acyclic Graphs for Oral Disease Research”

2016 ◽  
Vol 95 (11) ◽  
pp. 1315-1315
Author(s):  
A.A. Akinkugbe ◽  
S. Sharma ◽  
R. Ohrbach ◽  
G.D. Slade ◽  
C. Poole
Keyword(s):  
Directed Acyclic Graphs ◽  
Oral Disease ◽  
Disease Research ◽  
Letter To The Editor ◽  
Acyclic Graphs

scholarly journals Cross-Sectional Model-Building for Research on Subjective Well-Being: Gaining Clarity on Control Variables

2021 ◽  
Author(s):  
David Bartram
Keyword(s):  
Model Building ◽  
Well Being ◽  
Directed Acyclic Graphs ◽  
Subjective Well Being ◽  
Social Scientists ◽  
Cross Sectional ◽  
Acyclic Graphs ◽  
Standard Set ◽  
Substantial Bias ◽  
Parsimonious Model

AbstractHappiness/well-being researchers who use quantitative analysis often do not give persuasive reasons why particular variables should be included as controls in their cross-sectional models. One commonly sees notions of a “standard set” of controls, or the “usual suspects”, etc. These notions are not coherent and can lead to results that are significantly biased with respect to a genuine causal relationship.This article presents some core principles for making more effective decisions of that sort.  The contribution is to introduce a framework (the “causal revolution”, e.g. Pearl and Mackenzie 2018) unfamiliar to many social scientists (though well established in epidemiology) and to show how it can be put into practice for empirical analysis of causal questions.  In simplified form, the core principles are: control for confounding variables, and do not control for intervening variables or colliders.  A more comprehensive approach uses directed acyclic graphs (DAGs) to discern models that meet a minimum/efficient criterion for identification of causal effects.The article demonstrates this mode of analysis via a stylized investigation of the effect of unemployment on happiness.  Most researchers would include other determinants of happiness as controls for this purpose.  One such determinant is income—but income is an intervening variable in the path from unemployment to happiness, and including it leads to substantial bias.  Other commonly-used variables are simply unnecessary, e.g. religiosity and sex.  From this perspective, identifying the effect of unemployment on happiness requires controlling only for age and education; a small (parsimonious) model is evidently preferable to a more complex one in this instance.

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