{"title":"A Survey of Benchmarks for Graph-Processing Systems","authors":"A. Bonifati, G. Fletcher, J. Hidders, A. Iosup","doi":"10.1007/978-3-319-96193-4_6","DOIUrl":"https://doi.org/10.1007/978-3-319-96193-4_6","url":null,"abstract":"","PeriodicalId":227251,"journal":{"name":"Graph Data Management","volume":"3 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2018-11-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"123028128","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
{"title":"An introduction to Graph Data Management","authors":"Renzo Angles, Claudio Gutiérrez","doi":"10.1007/978-3-319-96193-4_1","DOIUrl":"https://doi.org/10.1007/978-3-319-96193-4_1","url":null,"abstract":"","PeriodicalId":227251,"journal":{"name":"Graph Data Management","volume":"45 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2017-12-29","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"122788236","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
{"title":"Labelling-Scheme-based Subgraph Query Processing on Graph Data","authors":"Hongzhi Wang, Jianzhong Li, Hong Gao","doi":"10.4018/978-1-61350-053-8.ch007","DOIUrl":"https://doi.org/10.4018/978-1-61350-053-8.ch007","url":null,"abstract":"When data are modeled as graphs, many research issues arise. In particular, there are many new challenges in query processing on graph data. This chapter studies the problem of structural queries on graph data. A hash-based structural join algorithm, HGJoin, is first proposed to handle reachability queries on graph data. Then, it is extended to the algorithms to process structural queries in form of bipartite graphs. Finally, based on these algorithms, a strategy to process subgraph queries in form of general DAGs is proposed. It is notable that all the algorithms above can be slightly modified to process structural queries in form of general graphs. DOI: 10.4018/978-1-61350-053-8.ch007","PeriodicalId":227251,"journal":{"name":"Graph Data Management","volume":"14 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"1900-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"123892124","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
{"title":"An Overview of Graph Indexing and Querying Techniques","authors":"S. Sakr, Ghazi Al-Naymat","doi":"10.4018/978-1-61350-053-8.ch004","DOIUrl":"https://doi.org/10.4018/978-1-61350-053-8.ch004","url":null,"abstract":"Recently, there has been a lot of interest in the application of graphs in different domains. Graphs have been widely used for data modeling in different application domains such as: chemical compounds, protein networks, social networks and Semantic Web. Given a query graph, the task of retrieving related graphs as a result of the query from a large graph database is a key issue in any graph-based application. This has raised a crucial need for efficient graph indexing and querying techniques. In this chapter, we provide an overview of different techniques for indexing and querying graph databases. An overview of several proposals of graph query language is also given. Finally, we provide a set of guidelines for future research directions.","PeriodicalId":227251,"journal":{"name":"Graph Data Management","volume":"54 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"1900-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"123628233","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
David Dominguez-Sal, V. Muntés-Mulero, Norbert Martínez-Bazan, J. Larriba-Pey
{"title":"Graph Representation","authors":"David Dominguez-Sal, V. Muntés-Mulero, Norbert Martínez-Bazan, J. Larriba-Pey","doi":"10.4018/978-1-61350-053-8.ch001","DOIUrl":"https://doi.org/10.4018/978-1-61350-053-8.ch001","url":null,"abstract":"","PeriodicalId":227251,"journal":{"name":"Graph Data Management","volume":"01 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"1900-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"127233787","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
{"title":"Matrix Decomposition-based Dimensionality Reduction on Graph Data","authors":"Hiroto Saigo, K. Tsuda","doi":"10.4018/978-1-61350-053-8.ch011","DOIUrl":"https://doi.org/10.4018/978-1-61350-053-8.ch011","url":null,"abstract":"Graph is a mathematical framework that allows us to represent and manage many real-world data such as relational data, multimedia data and biomedical data. When each data point is represented as a graph and we are given a number of graphs, a task is to extract a few common patterns that capture the property of each population. A frequent graph mining algorithm such as AGM, gSpan and Gaston can enumerate all the frequent patterns in graph data, however, the number of patterns grows exponentially, therefore it is essential to output only discriminative patterns. There are many existing researches on this topic, but this chapter focus on the use of matrix decomposition techniques, and explains the two general cases where either i) no target label is available, or ii) target label is available for each data point. The reuslting method is a branch and bound pattern mining algorithm with efficient pruning condition, and we evaluate its effectiveness on cheminformatics data. DOI: 10.4018/978-1-61350-053-8.ch011","PeriodicalId":227251,"journal":{"name":"Graph Data Management","volume":"72 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"1900-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"128073704","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Maria-Esther Vidal, Amadís Martínez, E. Ruckhaus, Tomas Lampo, Javier Sierra
{"title":"On the Efficiency of Querying and Storing RDF Documents","authors":"Maria-Esther Vidal, Amadís Martínez, E. Ruckhaus, Tomas Lampo, Javier Sierra","doi":"10.4018/978-1-61350-053-8.ch016","DOIUrl":"https://doi.org/10.4018/978-1-61350-053-8.ch016","url":null,"abstract":"In the context of the Semantic Web, different approaches have been defined to represent RDF documents, and the selected representation affects storage and time complexity of the RDF data recovery and query processing tasks. This chapter addresses the problem of efficiently querying and storing RDF documents, and presents an alternative representation of RDF data, Bhyper, which is based on hypergraphs. Additionally, access and optimization techniques to efficiently execute queries with low cost, are defined on top of this hypergraph based representation. The chapter’s authors have empirically studied the performance of the Bhyper based techniques, and their experimental results show that the proposed hypergraph based formalization reduces the RDF data access time as well as the space needed to store the Bhyper structures, while the query execution time of state-the-of-art RDF engines can be sped up by up to two orders of magnitude. DOI: 10.4018/978-1-61350-053-8.ch016","PeriodicalId":227251,"journal":{"name":"Graph Data Management","volume":"12 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"1900-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"115013174","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
{"title":"Graph applications in chemoinformatics and structural bioinformatics","authors":"Eleanor J. Gardiner","doi":"10.4018/978-1-61350-053-8.ch017","DOIUrl":"https://doi.org/10.4018/978-1-61350-053-8.ch017","url":null,"abstract":"INTRODUCTION Chemistry space is exceedingly large. Recent estimates put the number of potentially 'drug-like' molecules at anything between 10 12 and 10 180 (Gorse, 2006). The overwhelming majority of these molecules never has been, and never will be, synthesized but methods are nevertheless required to determine which of these potential compounds should be made. Some large pharmaceutical/ agrochemical companies maintain corporate databases of millions of molecules. The discovery of New Chemical Entities (NCEs) which may become drugs depends on the successful mining ABSTRACT The focus of this chapter will be the uses of graph theory in chemoinformatics and in structural bio-informatics. There is a long history of chemical graph theory dating back to the 1860's and Kekule's structural theory. It is natural to regard the atoms of a molecule as nodes and the bonds as edges (2D representations) of a labeled graph (a molecular graph). This chapter will concentrate on the algorithms developed to exploit the computer representation of such graphs and their extensions in both two and three dimensions (where an edge represents the distance in 3D space between a pair of atoms), together with the algorithms developed to exploit them. The algorithms will generally be summarized rather than detailed. The methods were later extended to larger macromolecules (such as proteins); these will be covered in less detail. of the information stored in these databases. Such information may be explicit (e.g. the molecules may be annotated with chemical reaction or activity data) or may be implicit in the structure of a molecule. For many years isomorphism algorithms have formed the basis of structural comparison between pairs of molecules in these databases, designed to extract this implicit information. The main purpose of this chapter is to introduce the concept of chemoinformatics to practitioners from the field of graph theory and to demonstrate the widespread application of graph-theoretic techniques to the solving of chemoinformatics problems. However, many graph-theoretic algorithms from chemoinformatics have subsequently been adapted for the structural comparison of macromolecules in the field known as structural bioinformatics. A secondary aim of the chapter is to provide a brief overview of these applications. The layout of the chapter will now be described. In the Background section some definitions of chemoinformatics are given and the topic is placed within the context of the drug discovery process. Structural bioinformatics is also defined and the necessary graph theoretic notation is introduced. The two main …","PeriodicalId":227251,"journal":{"name":"Graph Data Management","volume":"43 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"1900-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"133615142","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
M. Mongiovì, G. Micale, A. Ferro, R. Giugno, A. Pulvirenti, D. Shasha
{"title":"gLabTrie: A Data Structure for Motif Discovery with Constraints","authors":"M. Mongiovì, G. Micale, A. Ferro, R. Giugno, A. Pulvirenti, D. Shasha","doi":"10.1007/978-3-319-96193-4_3","DOIUrl":"https://doi.org/10.1007/978-3-319-96193-4_3","url":null,"abstract":"","PeriodicalId":227251,"journal":{"name":"Graph Data Management","volume":"1 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"1900-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"129214279","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}