Exact and Inexact Graph Matching: Methodology and Applications

Riesen, Kaspar; Jiang, Xiaoyi; Bunke, Horst

doi:10.1007/978-1-4419-6045-0_7

Cited by 61 publications

(33 citation statements)

References 89 publications

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“…Particular aspects of graph querying have also been surveyed; for example, works by Bunke [2000], Gallagher [2006], Riesen et al [2010], Livi and Rizzi [2013], and Yan et al [2016] deal with particular aspects of graph pattern matching, while Yu and Cheng [2010] concentrate on graph reachability queries. Again, however, all such works have a narrower focus than our survey.…”

Section: Introductionmentioning

confidence: 99%

Foundations of Modern Query Languages for Graph Databases

et al. 2017

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We survey foundational features underlying modern graph query languages. We first discuss two popular graph data models: edge-labelled graphs, where nodes are connected by directed, labelled edges; and property graphs, where nodes and edges can further have attributes. Next we discuss the two most fundamental graph querying functionalities: graph patterns and navigational expressions. We start with graph patterns, in which a graph-structured query is matched against the data. Thereafter we discuss navigational expressions, in which patterns can be matched recursively against the graph to navigate paths of arbitrary length; we give an overview of what kinds of expressions have been proposed, and how they can be combined with graph patterns. We also discuss several semantics under which queries using the previous features can be evaluated, what effects the selection of features and semantics has on complexity, and offer examples of such features in three modern languages that are used to query graphs: SPARQL, Cypher and Gremlin. We conclude by discussing the importance of formalisation for graph query languages; a summary of what is known about SPARQL, Cypher and Gremlin in terms of expressivity and complexity; and an outline of possible future directions for the area.

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Section: Introductionmentioning

confidence: 99%

Foundations of Modern Query Languages for Graph Databases

et al. 2017

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“…the error-tolerant subgraph isomorphism with the least expensive sequence of edit operations. In other words, we want to find the minimum amount of distortion needed to transform a Prism into the actual mobile context, thus computing their graph edit distance [21]:…”

Section: Definition 7 (Cost Of Error-tolerant Rdf Subgraph Isomorphism)mentioning

confidence: 99%

Error-Tolerant RDF Subgraph Matching for Adaptive Presentation of Linked Data on Mobile

Costabello

2014

Lecture Notes in Computer Science

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Abstract. We present PRISSMA, a context-aware presentation layer for Linked Data. PRISSMA extends the Fresnel vocabulary with the notion of mobile context. Besides, it includes an algorithm that determines whether the sensed context is compatible with some context declarations. The algorithm finds optimal error-tolerant subgraph isomorphisms between RDF graphs using the notion of graph edit distance and is sublinear in the number of context declarations in the system.

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“…However, due to NP-completeness of the problem and the computational complexity inherent in such API call graph matching algorithms [10], they are prohibitively expensive to use for large graphs. This study proposes a system to solve the problem of malware detection based on API call graph achieving better detection rate and performance levels.…”

Section: Figure 1 Organization Of Malware Detectionmentioning

confidence: 99%

“…Matching can be classified into two categories, namely, exact matching and inexact matching. Exact graph matching applies when the two graphs has the same number of vertices, whereas in inexact graph matching the two graphs has different number of vertices [10].…”

Section: Introductionmentioning

confidence: 99%