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Lyndon M. Henry

dblp:331/0990 · DBLP profile ↗
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2ranked-venue papers
0as first author
2since 2021 · last 2023
0000-0001-7328-6378ORCID · corroborated

Domains — the database's venue-derived domains; a paper can count in several

Databases, data management, data science and information retrieval · 2 · 2 since 2021

Expertise — from the expertise taxonomy: the topics of the expert's papers under the CCF categories. A weight counts papers with recency: 1 for a paper about the topic, 0.3 when the topic is its context, halved every five years.

Theoretical computer science
2 papers
Graph algorithms and graph theory · 68% Automated reasoning and model checking · 32%
Databases, data mining, and information retrieval
1 paper
Graph data management · 100%
Software engineering, system software, and programming languages
1 paper
Program analysis · 100%

Topics — the 5 heaviest of 5, each with the papers that count most for it

TopicWeightPapersLastEvidence papers
Graph algorithms and graph theory › graph algorithms › transitive closure
graph reachability
0.712023
Efficient Sink-Reachability Analysis via Graph Reduction (Extended Abstract) · ICDE 2023
Graph algorithms and graph theory › graph theory › graph transformation
graph reduction
0.612022
Efficient Sink-Reachability Analysis via Graph Reduction · IEEE Trans. Knowl. Data Eng. 2022
Automated reasoning and model checking
reachability
0.612022
Efficient Sink-Reachability Analysis via Graph Reduction · IEEE Trans. Knowl. Data Eng. 2022
Graph data management › graph transformation
graph reduction
0.212023
Efficient Sink-Reachability Analysis via Graph Reduction (Extended Abstract) · ICDE 2023
Program analysis
static analysis
0.212022
Efficient Sink-Reachability Analysis via Graph Reduction · IEEE Trans. Knowl. Data Eng. 2022

Methods — techniques the papers use, named apart from their topics

graph reduction · 1.3condensation · 1.3condensation operators · 1.1compositional framework · 1.1
YearPublicationVenuePosition
2023 Efficient Sink-Reachability Analysis via Graph Reduction (Extended Abstract)
abstract
We study a variation of the elementary graph reachability problem, called the sink-reachability problem, which can be found in many applications such as static program analysis, social network analysis, large scale web graph analysis, XML document link path analysis, and the study of gene regulation relationships. To scale sink-reachablity analysis to large graphs, we develop a highly scalable sink-reachability preserving graph reduction strategy for input sink graphs, by using a composition framework. That is, individual sink-reachability preserving condensation operators, each running in linear time, are pipelined together to produce graph reduction algorithms that result in close to maximum reduction, while keeping the computation efficient. Experiments on large real-world sink graphs demonstrate that our compositional approach achieves a reduction rate of up to 99.74% for vertices and a rate of up to 99.46% for edges.
Jens Dietrich 0001, Lijun Chang, Lyndon M. Henry, Catherine McCartin, Bernhard Scholz
ICDE4
2022 Efficient Sink-Reachability Analysis via Graph Reduction
abstract
The reachability problem on directed graphs, asking whether two vertices are connected via a directed path, is an elementary problem that has been well-studied. In this paper, we study a variation of the elementary reachability problem, called thesink-reachabilityproblem, which can be found in many applications such as static program analysis, social network analysis, large scale web graph analysis, XML document link path analysis, and the study of gene regulation relationships. To scale sink-reachablity analysis to large graphs, we develop a highly scalablesink-reachability preservinggraph reduction strategy for input sink graphs, by using acompositionframework. That is, individual sink-reachability preserving condensation operators, each running in linear time, are pipelined together to produce graph reduction algorithms that result in close to maximum reduction, while keeping the computation efficient. Experiments on large real-world sink graphs demonstrate the efficiency and effectiveness of our compositional approach to sink-reachability preserving graph reduction with a reduction rate of up to 99.74 percent for vertices and a rate of up to 99.46 percent for edges.
Jens Dietrich 0001, Lijun Chang, Lyndon M. Henry, Catherine McCartin, Bernhard Scholz
IEEE Trans. Knowl. Data Eng.4