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gSpan: GraphBased Substructure Pattern Mining
, 2002
"... We investigate new approaches for frequent graphbased pattern mining in graph datasets and propose a novel algorithm called gSpan (graphbased Substructure pattern mining) , which discovers frequent substructures without candidate generation. gSpan builds a new lexicographic order among graphs, and ..."
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Cited by 650 (34 self)
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We investigate new approaches for frequent graphbased pattern mining in graph datasets and propose a novel algorithm called gSpan (graphbased Substructure pattern mining) , which discovers frequent substructures without candidate generation. gSpan builds a new lexicographic order among graphs
Chromatic number and complete graph substructures for degree sequences
, 2009
"... Given a graphic degree sequence D, let χ(D) (respectively ω(D), h(D), and H(D)) denote the maximum value of the chromatic number (respectively, the size of the largest clique, largest clique subdivision, and largest clique minor) taken over all simple graphs whose degree sequence is D. It is proved ..."
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Cited by 3 (0 self)
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Given a graphic degree sequence D, let χ(D) (respectively ω(D), h(D), and H(D)) denote the maximum value of the chromatic number (respectively, the size of the largest clique, largest clique subdivision, and largest clique minor) taken over all simple graphs whose degree sequence is D. It is proved
Applying Algebraic Mining Method of Graph Substructures to Mutageniesis Data Analysis
"... this paper, one graph constitutes one transaction. The graph structured data can be transformed without much computational effort into an adjacency matrix whichisavery well known representation of a graph in mathematical graph theory[2]. A node which corresponds to the ith row (the ..."
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Cited by 1 (0 self)
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this paper, one graph constitutes one transaction. The graph structured data can be transformed without much computational effort into an adjacency matrix whichisavery well known representation of a graph in mathematical graph theory[2]. A node which corresponds to the ith row (the
An Aprioribased Algorithm for Mining Frequent Substructures from Graph Data
, 2000
"... This paper proposes a novel approach named AGM to efficiently mine the association rules among the frequently appearing substructures in a given graph data set. A graph transaction is represented by an adjacency matrix, and the frequent patterns appearing in the matrices are mined through the exte ..."
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Cited by 310 (7 self)
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This paper proposes a novel approach named AGM to efficiently mine the association rules among the frequently appearing substructures in a given graph data set. A graph transaction is represented by an adjacency matrix, and the frequent patterns appearing in the matrices are mined through
Substructure similarity search in graph databases
 In SIGMOD
, 2005
"... Advanced database systems face a great challenge raised by the emergence of massive, complex structural data in bioinformatics, cheminformatics, and many other applications. The most fundamental support needed in these applications is the efficient search of complex structured data. Since exact mat ..."
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Cited by 90 (6 self)
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matching is often too restrictive, similarity search of complex structures becomes a vital operation that must be supported efficiently. In this paper, we investigate the issues of substructure similarity search using indexed features in graph databases. By transforming the edge relaxation ratio of a query
Substructure Discovery Using Minimum Description Length and Background Knowledge
 Journal of Artificial Intelligence Research
, 1994
"... The ability to identify interesting and repetitive substructures is an essential component to discovering knowledge in structural data. We describe a new version of our Subdue substructure discovery system based on the minimum description length principle. The Subdue system discovers substructures ..."
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Cited by 199 (44 self)
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that compress the original data and represent structural concepts in the data. By replacing previouslydiscovered substructures in the data, multiple passes of Subdue produce a hierarchical description of the structural regularities in the data. Subdue uses a computationallybounded inexact graph match
Substructure discovery in the SUBDUE system
 In Proc. of the Workshop on Knowledge Discovery in Databases
, 1994
"... Because many databases contain or can be embellished with structural information, a method for identifying interesting and repetitive substructures is an essential component to discovering knowledge in such databases. This paper describes the SUBDUE system, which uses the minimum description length ..."
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Cited by 77 (3 self)
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of background knowledgeguides SUBDUE toward appropriate substructures for a particular domain or discovery goal, and the use of an inexact graph match allows a controlled amount of deviations in the instance of a substructure concept. We describe the application of SUBDUE to a variety of domains. We also
Graph Indexing: A Frequent Structurebased Approach
, 2004
"... Graph has become increasingly important in modelling complicated structures and schemaless data such as proteins, chemical compounds, and XML documents. Given a graph query, it is desirable to retrieve graphs quickly from a large database via graphbased indices. In this paper, we investigate the is ..."
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Cited by 201 (25 self)
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the issues of indexing graphs and propose a novel solution by applying a graph mining technique. Di#erent from the existing pathbased methods, our approach, called gIndex, makes use of frequent substructure as the basic indexing feature. Frequent substructures are ideal candidates since they explore
A SUBSTRUCTURAL LOGIC FOR LAYERED GRAPHS
"... Abstract. Complex systems, be they natural or synthetic, are ubiquitous. In particular, complex networks of devices and services underpin most of society’s operations. By their very nature, such systems are difficult to conceptualize and reason about effectively. The concept of layering is widespre ..."
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commutative substructural, separating logic. We provide soundness and completeness results for a class of algebraic models that includes layered graphs, which give a mathematically substantial semantics to this very weak logic. We explain, via examples, applications in information processing and security. 1.
Searching Substructures with Superimposed Distance
"... Efficient indexing techniques have been developed for the exact and approximate substructure search in large scale graph databases. Unfortunately, the retrieval problem of structures with categorical or geometric distance constraints is not solved yet. In this paper, we develop a method called PIS ( ..."
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Cited by 16 (0 self)
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Efficient indexing techniques have been developed for the exact and approximate substructure search in large scale graph databases. Unfortunately, the retrieval problem of structures with categorical or geometric distance constraints is not solved yet. In this paper, we develop a method called PIS
Results 1  10
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375