Results 1 - 10 of 375

gSpan: Graph-Based Substructure Pattern Mining

by Xifeng Yan, Jiawei Han , 2002
"... We investigate new approaches for frequent graph-based pattern mining in graph datasets and propose a novel algorithm called gSpan (graph-based Substructure pattern mining) , which discovers frequent substructures without candidate generation. gSpan builds a new lexicographic order among graphs, and ..."
Abstract - Cited by 650 (34 self) - Add to MetaCart
We investigate new approaches for frequent graph-based pattern mining in graph datasets and propose a novel algorithm called gSpan (graph-based 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

by Zdenek Dvorak, Bojan Mohar , 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 ..."
Abstract - Cited by 3 (0 self) - Add to MetaCart
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

by Akihiro Inokuchi, Takashi Washio, Takashi Okada, Hiroshi Motoda
"... 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 i-th row (the ..."
Abstract - Cited by 1 (0 self) - Add to MetaCart
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 i-th row (the

An Apriori-based Algorithm for Mining Frequent Substructures from Graph Data

by Akihiro Inokuchi, Takashi Washio, Hiroshi Motoda , 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 ..."
Abstract - Cited by 310 (7 self) - Add to MetaCart
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

by Xifeng Yan, Philip S. Yu, Jiawei Han - In SIGMOD , 2005
"... Advanced database systems face a great challenge raised by the emergence of massive, complex structural data in bioinformatics, chem-informatics, and many other applications. The most fundamental support needed in these applications is the efficient search of complex structured data. Since exact mat ..."
Abstract - Cited by 90 (6 self) - Add to MetaCart
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

by Diane J. Cook, Lawrence B. Holder - 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 ..."
Abstract - Cited by 199 (44 self) - Add to MetaCart
that compress the original data and represent structural concepts in the data. By replacing previously-discovered substructures in the data, multiple passes of Subdue produce a hierarchical description of the structural regularities in the data. Subdue uses a computationally-bounded inexact graph match

Substructure discovery in the SUBDUE system

by Lawrence B. Holder, Diane J. Cook, Surnjani Djoko - 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 ..."
Abstract - Cited by 77 (3 self) - Add to MetaCart
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 Structure-based Approach

by Xifeng Yan , Philip S. Yu, Jiawei Han , 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 graph-based indices. In this paper, we investigate the is ..."
Abstract - Cited by 201 (25 self) - Add to MetaCart
the issues of indexing graphs and propose a novel solution by applying a graph mining technique. Di#erent from the existing path-based 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

by Matthew Collinson, Kevin Mcdonald, David Pym
"... Abstract. Complex systems, be they natural or synthetic, are ubiquitous. In particular, com-plex 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

by Xifeng Yan, Feida Zhu, Jiawei Han, Philip S. Yu
"... 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 ( ..."
Abstract - Cited by 16 (0 self) - Add to MetaCart
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 of 375