Molecular feature mining in HIV data

Krämer, Stefan; Raedt, Luc De; Helma, Christoph

doi:10.1145/502512.502533

Cited by 171 publications

(156 citation statements)

References 20 publications

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“…Given the wide range of graph mining applications (e.g. detecting anomalies in network [12], Internet links analysis [13,14], graph query indexing [8,12,13,14], and medicine [15,16]), great deal of research works have been done on graphs. An interesting matter as to graph mining is graph containment problem.…”

Section: Introductionmentioning

confidence: 99%

A New Method for Graph Queries Processing without Index Reconstruction on Dynamic Graph Databases

Dinari¹

2017

IJMECS

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Abstract-Graphs play notable role in daily life. For instance, they are used in variety fields such as social networks, malware detection, and biological networks. Graph data processing performed to extract useful information is known as graph mining. A critical field of graph mining is graph containment problem, in which all graphs containing the query are returned by a graph query q. Scanning the whole database (graph query as a subgraph) for a query is a time consuming process. To improve query performance, an inverted index is constructed on the graph database and then the query is performed based on the query. The problem in this process is that when a graph is added to or removed from a database, the inverted index must be reconstructed. The present study proposes a method in which index updating is not needed upon a change in the database. This feature enables simultaneous inverted index updating and querying. The assessment results showed optimum and satisfactory performance of the proposed method.

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Section: Introductionmentioning

confidence: 99%

A New Method for Graph Queries Processing without Index Reconstruction on Dynamic Graph Databases

Dinari¹

2017

IJMECS

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“…However, the vast majority transfers techniques developed originally for frequent item set mining. 1 Examples include MolFea [10], FSG [11], MoSS/MoFa [1], gSpan [14], CloseGraph [15], FFSM [8], and Gaston [12]. A related approach is used in Subdue [4].…”

Section: Introductionmentioning

confidence: 99%

Canonical Forms for Frequent Graph Mining

Borgelt

2007

Studies in Classification, Data Analysis, and Knowledge Organization

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Summary.A core problem of approaches to frequent graph mining, which are based on growing subgraphs into a set of graphs, is how to avoid redundant search. A powerful technique for this is a canonical description of a graph, which uniquely identifies it, and a corresponding test. I introduce a family of canonical forms based on systematic ways to construct spanning trees. I show that the canonical form used in gSpan [14] is a member of this family, and that MoSS/MoFa [1, 3] is implicitly based on a different member, which I make explicit and exploit in the same way.

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“…Such graphs arise naturally in a number of different application domains including network intrusion [33,28], semantic web [3], behavioral modeling [43,36], VLSI reverse engineering [46], link analysis [24,27,26,38], chemical compound classification [8,29,14,9], and macromolecule analysis [39]. Moreover, they can be used to effectively model the structural and relational characteristics of a variety of datasets arising in other areas such as physical sciences (e.g., chemistry, fluid dynamics, astronomy, structural mechanics, and ecosystem modeling), life sciences (e.g., genomics, proteomics, pharmacogenomics, and health informatics), and home-land defense (e.g., information assurance, network intrusion, infrastructure protection, and terrorist-threat prediction/identification).…”

Section: Introductionmentioning

confidence: 99%

Finding Frequent Patterns in a Large Sparse Graph

Kuramochi

Karypis

2004

Proceedings of the 2004 SIAM International Conference on Data Mining

134

180

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This paper presents two algorithms based on the horizontal and vertical pattern discovery paradigms that find the connected subgraphs that have a sufficient number of edge-disjoint embeddings in a single large undirected labeled sparse graph. These algorithms use three different methods to determine the number of the edge-disjoint embeddings of a subgraph that are based on approximate and exact maximum independent set computations and use it to prune infrequent subgraphs. Experimental evaluation on real datasets from various domains show that both algorithms achieve good performance, scale well to sparse input graphs with more than 100,000 vertices, and significantly outperform a previously developed algorithm.

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Molecular feature mining in HIV data

Cited by 171 publications

References 20 publications

A New Method for Graph Queries Processing without Index Reconstruction on Dynamic Graph Databases

A New Method for Graph Queries Processing without Index Reconstruction on Dynamic Graph Databases

Canonical Forms for Frequent Graph Mining

Finding Frequent Patterns in a Large Sparse Graph

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