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Graph Transformation

Overview of attention for book
Cover of 'Graph Transformation'

Table of Contents

  1. Altmetric Badge
    Book Overview
  2. Altmetric Badge
    Chapter 1 Parameterized Verification and Model Checking for Distributed Broadcast Protocols
  3. Altmetric Badge
    Chapter 2 Tableau-Based Reasoning for Graph Properties
  4. Altmetric Badge
    Chapter 3 Verifying Monadic Second-Order Properties of Graph Programs
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    Chapter 4 Generating Abstract Graph-Based Procedure Summaries for Pointer Programs
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    Chapter 5 Generating Inductive Predicates for Symbolic Execution of Pointer-Manipulating Programs
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    Chapter 6 Attribute Handling for Generating Preconditions from Graph Constraints
  8. Altmetric Badge
    Chapter 7 From Core OCL Invariants to Nested Graph Constraints
  9. Altmetric Badge
    Chapter 8 Specification and Verification of Graph-Based Model Transformation Properties
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    Chapter 9 A Static Analysis of Non-confluent Triple Graph Grammars for Efficient Model Transformation
  11. Altmetric Badge
    Chapter 10 Transformation and Refinement of Rigid Structures
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    Chapter 11 Reversible Sesqui-Pushout Rewriting
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    Chapter 12 On Pushouts of Partial Maps
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    Chapter 13 The Subgraph Isomorphism Problem on a Class of Hyperedge Replacement Languages
  15. Altmetric Badge
    Chapter 14 Canonical Derivations with Negative Application Conditions
  16. Altmetric Badge
    Chapter 15 Van Kampen Squares for Graph Transformation
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    Chapter 16 Graph Transformation Meets Reversible Circuits: Generation, Evaluation, and Synthesis
  18. Altmetric Badge
    Chapter 17 Towards Process Mining with Graph Transformation Systems
  19. Altmetric Badge
    Chapter 18 Jerboa: A Graph Transformation Library for Topology-Based Geometric Modeling
Attention for Chapter 9: A Static Analysis of Non-confluent Triple Graph Grammars for Efficient Model Transformation
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1 X user

Citations

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Chapter title
A Static Analysis of Non-confluent Triple Graph Grammars for Efficient Model Transformation
Chapter number 9
Book title
Graph Transformation
Published in
Lecture notes in computer science, July 2014
DOI 10.1007/978-3-319-09108-2_9
Book ISBNs
978-3-31-909107-5, 978-3-31-909108-2
Authors

Anthony Anjorin, Erhan Leblebici, Andy Schürr, Gabriele Taentzer, Anjorin, Anthony, Leblebici, Erhan, Schürr, Andy, Taentzer, Gabriele

X Demographics

X Demographics

The data shown below were collected from the profile of 1 X user who shared this research output. Click here to find out more about how the information was compiled.
Mendeley readers

Mendeley readers

The data shown below were compiled from readership statistics for 9 Mendeley readers of this research output. Click here to see the associated Mendeley record.

Geographical breakdown

Country Count As %
Unknown 9 100%

Demographic breakdown

Readers by professional status Count As %
Student > Master 3 33%
Student > Ph. D. Student 2 22%
Student > Bachelor 1 11%
Student > Doctoral Student 1 11%
Researcher 1 11%
Other 0 0%
Unknown 1 11%
Readers by discipline Count As %
Computer Science 7 78%
Psychology 1 11%
Unknown 1 11%
Attention Score in Context

Attention Score in Context

This research output has an Altmetric Attention Score of 1. This is our high-level measure of the quality and quantity of online attention that it has received. This Attention Score, as well as the ranking and number of research outputs shown below, was calculated when the research output was last mentioned on 23 July 2014.
All research outputs
#15,303,056
of 22,758,963 outputs
Outputs from Lecture notes in computer science
#4,648
of 8,126 outputs
Outputs of similar age
#132,208
of 228,546 outputs
Outputs of similar age from Lecture notes in computer science
#101
of 210 outputs
Altmetric has tracked 22,758,963 research outputs across all sources so far. This one is in the 22nd percentile – i.e., 22% of other outputs scored the same or lower than it.
So far Altmetric has tracked 8,126 research outputs from this source. They receive a mean Attention Score of 5.0. This one is in the 27th percentile – i.e., 27% of its peers scored the same or lower than it.
Older research outputs will score higher simply because they've had more time to accumulate mentions. To account for age we can compare this Altmetric Attention Score to the 228,546 tracked outputs that were published within six weeks on either side of this one in any source. This one is in the 32nd percentile – i.e., 32% of its contemporaries scored the same or lower than it.
We're also able to compare this research output to 210 others from the same source and published within six weeks on either side of this one. This one is in the 33rd percentile – i.e., 33% of its contemporaries scored the same or lower than it.