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About 10,000 papers for “graph construction”

Graph Construction

Simone Teufel, Slides After Frank Keller, Graph Connectivity · 2011

Cited by 0

Graph Construction

Amarnag Subramanya, Partha Talukdar · Synthesis lectures on artificial intelligence and machine learning · 2014

Cited by 0

Graph construction algorithm.

Aleksandra Swiercz (3502541), Wojciech Frohmberg (3167223), Michal Kierzynka (5638562) et al. · Figshare · 2018

Cited by 0Open access

Generic graph construction

James E. Baumgartner · Journal of Symbolic Logic · 1984

Abstract It is shown that if ZF is consistent, then so is ZFC + GCH + “There is a graph with cardinality ℵ 2 and chromatic number ℵ 2 such that every subgraph of cardinality ≤ ℵ 1 , has chromatic number ≤ ℵ 0 ”. This partially answers a question of Erdös and Hajnal.

Cited by 19

Graph Real Construction

Goldman, Daniel S · OSF Preprints (OSF Preprints) · 2025

Building a general theory of a field of graph-like objects.

Cited by 0Open access

Abstract Planning Graph Construction

Pascal Bercher, Susanne Biundo · 2013

We present a heuristic for hybrid planning with preferences on final states. DomainTransformation The planning graph can be used for a (relaxed) reachability analysis. • It can be used for hybrid planning and for POCL planning, • it reduces the problem of estimating the quality of a task network to the problem of estimating the quality of a state, and • it performs a reachability analysis based on a planning graph [2]. Hybrid Planning with Preferences (Problem Definition) Hybrid planning [1] fuses HTN planning [3] with POCL planning [4]. A hybrid planning problem is a tuple (P,Tp,Tc,M,sinit,TNinit,g) with: • P is a set of atomic, ground propositions, • Tp,Tc ⊆ 2 P × 2 P × 2 P are sets of primitive and compound task schemata, resp., • M ⊆ Tc × TN is a set of decomposition methods, • sinit ∈ 2 P is the initial state, • TNinit ∈ TN is the the initial partial plan, and Given π = (P,Tp,Tc,M,sinit,TNinit,g) and a task network TN = (T,≺,CL), we construct a (relaxed) classical planning problem π ′ , s.t.: • if TN can be refined to a solution of π, then π ′ is solvable as well, and • every solution of π ′ is a refinement of TN w.r.t. its primitive tasks. π ′ = (P′,T ′ p,s ′ init,g′) is given by:

Cited by 0

PRINCIPLES OF GRAPH CONSTRUCTION

Frank E Harrell, Biostat. Mc. V · 2015

The ability to construct clear and informative graphs is related to the ability to understand the data. There are many excellent texts on statistical graphics (many of which are listed at the end of this chapter). Some of the best are Cleveland’s 1994 book The Elements of Graphing Data and the books by Tufte. The sugges-tions for making good statistical graphics outlined here are heavily influenced by Cleveland’s 1994 book. See also the excellent special issue of Journal of Computa-

Cited by 0

Fast kNN graph construction

Kassim Bin Mohamad Malaysia · DR-NTU (Nanyang Technological University) · 2024

This Final Year Project (FYP) report delves into a new Approximate Nearest Neighbor (ANN) searching algorithm, The Dense Graph- Parallel Approximate Nearest Neighbor Search Over Graphs (DG-PANN). It is an innovative enhancement of Parallel Approximate Nearest Neighbor Search Over Graphs (PANN). By leveraging a density-based strategy, DG-PANN has significantly accelerated the graph construction timing and improved the search query speed while maintaining relatively high recall values. Through thorough benchmarking with datasets such as Mnist and SensIT, DG-PANN has provided a better efficiency in both the graph construction phase and search query phase as compared to the traditional PANN. The results have revealed that DG-PANN particularly excels in high-dimensional environments but its performance in lower-dimensional datasets, especially in the search query phase, leaves for more room for improvements. This report details DG-PANN’s design, implementation, result analysis, offering its strength and weaknesses and future improvements that broaden DG-PANN’s capabilities.

Cited by 0

Program Flow Graph Construction

For Static Analysis, Dale Shires (presenting · 2007

The Message Passing Interface #MPI# has been widely used to develop e#cient and portable parallel programs for distributed memory multiprocessors and workstation#PC clusters. In this paper, we present an algorithm for building a program #ow graph representation of an MPI program. As an extension of the control #ow graph representation of sequential codes, this representation provides a basis for important program analyses useful in software testing, debugging and code optimization.

Cited by 0

Probabilistic Knowledge Graph Construction

Dongwoo Kim, Lexing Xie, Cheng Soon Ong · arXiv (Cornell University) · 2016

Knowledge graph construction consists of two tasks: extracting information from external resources (knowledge population) and inferring missing information through a statistical analysis on the extracted information (knowledge completion). In many cases, insufficient external resources in the knowledge population hinder the subsequent statistical inference. The gap between these two processes can be reduced by an incremental population approach. We propose a new probabilistic knowledge graph factorisation method that benefits from the path structure of existing knowledge (e.g. syllogism) and enables a common modelling approach to be used for both incremental population and knowledge completion tasks. More specifically, the probabilistic formulation allows us to develop an incremental population algorithm that trades off exploitation-exploration. Experiments on three benchmark datasets show that the balanced exploitation-exploration helps the incremental population, and the additional path structure helps to predict missing information in knowledge completion.

Cited by 11Open access

Clustering and graph construction.

Guorong Wu (307379), Xuewei Peng (847584), Shihui Ying (847585) et al. · Figshare · 2016

<p>(a) Images are clustered into 2 subgroups, represented with different markers and colors, by using affinity propagation. (b) Intra-subgroup (black color) and inter-subgroup (orange color) connections in the graph.</p>

Cited by 0Open access

Degree-based graph construction

Hyunju Kim, Zoltán Toroczkai, Péter L. Erdős et al. · ArXiv.org · 2009

Degree-based graph construction is an ubiquitous problem in network modeling, ranging from social sciences to chemical compounds and biochemical reaction networks in the cell. This problem includes existence, enumeration, exhaustive construction and sampling questions with aspects that are still open today. Here we give necessary and sufficient conditions for a sequence of nonnegative integers to be realized as a simple graph's degree sequence, such that a given (but otherwise arbitrary) set of connections from a arbitrarily given node are avoided. We then use this result to present a swap-free algorithm that builds {\em all} simple graphs realizing a given degree sequence. In a wider context, we show that our result provides a greedy construction method to build all the $f$-factor subgraphs embedded within $K_n\setminus S_k$, where $K_n$ is the complete graph and $S_k$ is a star graph centered on one of the nodes.

Cited by 0Open access

Degree-based graph construction

Kim, P., Toroczkai, Z., Erdős, P. et al. · SZTAKI Publication Repository (Hungarian Academy of Sciences) · 2009

Cited by 0

Mako Call Graph Construction

Anonymous · Zenodo (CERN European Organization for Nuclear Research) · 2025

Mako is a practical analyzer for type-based indirect call resolution.

Cited by 0Open access