Graph Data Management最新文献_第2页

Large Scale Graph Mining with MapReduce: Counting Triangles in Large Real Networks 基于MapReduce的大规模图挖掘:大型真实网络中的三角形计数

Graph Data Management Pub Date : 1900-01-01 DOI: 10.4018/978-1-61350-053-8.ch013

Charalampos E. Tsourakakis

{"title":"Large Scale Graph Mining with MapReduce: Counting Triangles in Large Real Networks","authors":"Charalampos E. Tsourakakis","doi":"10.4018/978-1-61350-053-8.ch013","DOIUrl":"https://doi.org/10.4018/978-1-61350-053-8.ch013","url":null,"abstract":"In recent years, a considerable amount of research has focused on the study of graph structures arising from technological, biological and sociological systems. Graphs are the tool of choice in modeling such systems since they are typically described as sets of pairwise interactions. Important examples of such datasets are the Internet, the Web, social networks, and large-scale information networks which reach the planetary scale, e.g., Facebook and LinkedIn. The necessity to process large datasets, including graphs, has led to a major shift towards distributed computing and parallel applications, especially in the recent years. MapReduce was developed by Google, one of the largest users of multiple processor computing in the world, for facilitating the development of scalable and fault tolerant applications. MapReduce has become the de facto standard for processing large scale datasets both in industry and academia. In this Chapter, we present state of the art work on large scale graph mining using MapReduce. We survey research work on an important graph mining problem, counting the number of triangles in large-real world networks. We present the most important applications related to the count of triangles and two families of algorithms, a spectral and a combinatorial one, which solve the problem efficiently.","PeriodicalId":227251,"journal":{"name":"Graph Data Management","volume":"42 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":"114593100","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}

引用次数: 0

Data, Storage and Index Models for Graph Databases 图数据库的数据、存储和索引模型

Graph Data Management Pub Date : 1900-01-01 DOI: 10.4018/978-1-61350-053-8.ch003

S. Srinivasa

引用次数: 16

Querying RDF Data 查询RDF数据

Graph Data Management Pub Date : 1900-01-01 DOI: 10.4018/978-1-61350-053-8.ch015

Faisal Alkhateeb, J. Euzenat

引用次数: 9

TEDI: Efficient Shortest Path Query Answering on Graphs 图上的高效最短路径查询回答

Graph Data Management Pub Date : 1900-01-01 DOI: 10.4018/978-1-61350-053-8.ch009

Fang Wei-Kleiner

{"title":"TEDI: Efficient Shortest Path Query Answering on Graphs","authors":"Fang Wei-Kleiner","doi":"10.4018/978-1-61350-053-8.ch009","DOIUrl":"https://doi.org/10.4018/978-1-61350-053-8.ch009","url":null,"abstract":"Efficient shortest path query answering in large graphs is enjoying a growing number of applications, such as ranked keyword search in databases, social networks, ontology reasoning and bioinformatics. A shortest path query on a graph finds the shortest path for the given source and target vertices in the graph. Current techniques for efficient evaluation of such queries are based on the pre-computation of compressed Breadth First Search trees of the graph. However, they suffer from drawbacks of scalability. To address these problems, we propose TEDI, an indexing and query processing scheme for the shortest path query answering. TEDI is based on the tree decomposition methodology. The graph is first decomposed into a tree in which the node (a.k.a. bag) contains more than one vertex from the graph. The shortest paths are stored in such bags and these local paths together with the tree are the components of the index of the graph. Based on this index, a bottom-up operation can be executed to find the shortest path for any given source and target vertices. Our experimental results show that TEDI offers ordersof-magnitude performance improvement over existing approaches on the index construction time, the index size and the query answering.","PeriodicalId":227251,"journal":{"name":"Graph Data Management","volume":"13 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":"114911612","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}

引用次数: 158

Clustering Vertices in Weighted Graphs 加权图中的聚类顶点

Graph Data Management Pub Date : 1900-01-01 DOI: 10.4018/978-1-61350-053-8.ch012

D. Wijaya, S. Bressan

引用次数: 0

Applications of Flexible Querying to Graph Data 灵活查询在图数据中的应用

Graph Data Management Pub Date : 1900-01-01 DOI: 10.1007/978-3-319-96193-4_4

A. Poulovassilis

引用次数: 4

Business Process Graphs: Similarity Search and Matching 业务流程图:相似性搜索和匹配

Graph Data Management Pub Date : 1900-01-01 DOI: 10.4018/978-1-61350-053-8.ch018

R. Dijkman, M. Dumas, L. García-Bañuelos

引用次数: 7

Graph Mining Techniques: Focusing on discriminating between real and synthetic graphs 图挖掘技术:着重于区分真实图和合成图

Graph Data Management Pub Date : 1900-01-01 DOI: 10.4018/978-1-61350-053-8.ch010

A. P. Appel, C. Faloutsos, C. Traina

引用次数: 8

Shortest Path in Transportation Network and Weighted Subdivisions 交通网络最短路径与加权细分

Graph Data Management Pub Date : 1900-01-01 DOI: 10.4018/978-1-61350-053-8.ch020

Radwa El Shawi, Joachim Gudmundsson

引用次数: 2

A Survey of Relational Approaches for Graph Pattern Matching over Large Graphs 大图上图模式匹配的关系方法综述

Graph Data Management Pub Date : 1900-01-01 DOI: 10.4018/978-1-61350-053-8.ch006

Jiefeng Cheng, J. Yu

引用次数: 6