big graph mining

big graph mining

Big Graph Mining: Frameworks and Techniques -

2016-12-1  Big graph mining is an important research area and it has attracted considerable attention. It allows to process, analyze, and extract meaningful information from large amounts of graph data. Big graph mining has been highly motivated not only by the tremendously increasing size of graphs but also by its huge number of applications.

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Big Graph Mining: Algorithms and Discoveries

2021-2-8  There are several works on distributed big graph mining which can be grouped into two: (1) one not based on MapRe-duce/Hadoop, and (2) the other on top of it. The works not based on MapReduce/Hadoop include GraphLab, Pregel, and Trinity. GraphLab [34] provides a framework for parallel machine learning and data mining, in a shared mem-ory setting.

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Big Graph Mining: Algorithms and Discoveries

2015-10-22  2.2 Distributed Big Graph Mining There are several works on distributed big graph miningwhich can be grouped into two: (1) one not based onMapRe-duce/Hadoop, and (2) the other on

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Big graph mining: algorithms and discoveries: ACM

2013-4-30  Mining big graphs leads to many interesting applications including cyber security, fraud detection, Web search, recommendation, and many more. In this paper we describe Pegasus, a big graph mining system built on top of MapReduce, a modern distributed data processing platform.

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[1602.03072v1] Big Graph Mining: Frameworks and

2016-2-10  Abstract: Big graph mining is an important research area and it has attracted considerable attention. It allows to process, analyze, and extract meaningful information from large amounts of graph data. Big graph mining has been highly motivated not only by the tremendously increasing size of graphs but also by its huge number of applications.

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Big Data and Graph Mining - ITU

2018-9-5  Big Data and Graph Mining. REGIONALSTANDARDIZATIONFORUM(RSF) FORASIA. Big Data and Graph Mining. Lv Shaoqing. Deputy Director of IoT Experiment Center, Xi'an University of Posts and Telecommunications , China. REGIONALSTANDARDIZATIONFORUM(RSF) FORASIA.

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G-thinker: Big Graph Mining Made Easier and Faster

2021-5-11  intensive graph mining tasks that find from a big graph all subgraphs that satisfy certain requirements (e.g., graph matching and community detection). Due to the broad range of applications of such tasks, many single-threaded algorithms have been proposed. However, graphs such as online social networks and knowledge graphs often have

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Big graph mining for the web and social media ...

2014-2-24  Our tutorial consists of three main parts. We start with scalable graph mining algorithms for billion-scale graphs, in- cluding structure analysis, eigensolvers, storage and index- ing, and graph layout and graph compression. Next we de- scribe anomaly detection techniques for large scale graphs with applications on social media.

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[1709.03110] G-thinker: Big Graph Mining Made

2017-9-10  G-thinker provides an intuitive graph-exploration API for the convenient implementation of various graph mining algorithms, and the runtime engine provides efficient execution with bounded memory consumption, light network communication, and

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讲座:Big Graph Processing and Mining: Applications and ...

2021-5-20  题 目:Big Graph Processing and Mining: Applications and Advances. 嘉 宾: Xuemin Lin (林学民) Scientia Professor, Head of Database and Knowledge Research Group. The University of New South Wales. 主持人: 陈方若 上海交通大学安泰经济与管理学院院长、光启讲席教授. 时 间:2021 年 5 月 25 日(周二) 16:00-17:30. 地 点:上海交通大学 徐汇校区包图A403.

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Big Graph Mining: Algorithms and Discoveries

2015-10-22  Big Graph Mining: Algorithms and Discoveries U Kang and Christos Faloutsos Carnegie Mellon University {ukang, christos}@cs.cmuABSTRACT ...

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[1602.03072v1] Big Graph Mining: Frameworks and

2016-2-10  Big graph mining is an important research area and it has attracted considerable attention. It allows to process, analyze, and extract meaningful information from large amounts of graph data. Big graph mining has been highly motivated not only by the tremendously increasing size of graphs but also by its huge number of applications. Such applications include bioinformatics, chemoinformatics ...

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Big Graph Mining: Frameworks and Techniques

Big graph mining is an important research area and it has at-tracted considerable attention. It allows to process, analyze,and extract meaningful information from large amounts ofgraph data. Big graph mining has been highly motivated notonly by the tremendously increasing size of graphs but alsoby its huge number of applications. Such applications in-clude bioinformatics, chemoinformatics and ...

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Big graph mining for the web and social media ...

2014-2-24  Big graph mining for the web and social media: algorithms, anomaly detection, and applications. Pages 677–678. Previous Chapter Next Chapter. ABSTRACT. Graphs are everywhere: social networks, computer net- works, mobile call networks, the World Wide Web, protein interaction networks, and many more. The lower cost of disk storage, the success ...

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G-thinker: Big Graph Mining Made Easier and Faster

2021-5-11  G-thinker: Big Graph Mining Made Easier and Faster Da Yan§†1, Hongzhi Chen§2, James Cheng§3, M. Tamer Ozsu¨ ‡4, Qizhen Zhang§5, John C. S. Lui§6 §Department of Computer Science and Engineering, The Chinese University of Hong Kong f1yanda, 2hzchen, 3jcheng, 5qzzhang, [email protected] †Department of Computer and Information Sciences, The University of

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[PDF] Big graph mining for the web and social media ...

Graphs are everywhere: social networks, computer net- works, mobile call networks, the World Wide Web, protein interaction networks, and many more. The lower cost of disk storage, the success of social networking websites and Web 2.0 applications, and the high availability of data sources lead to graphs being generated at unprecedented size. They are now measured in terabytes or even petabytes ...

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[1709.03110] G-thinker: Big Graph Mining Made

2017-9-10  Abstract: This paper proposes a general system for compute-intensive graph mining tasks that find from a big graph all subgraphs that satisfy certain requirements (e.g., graph matching and community detection). Due to the broad range of applications of such tasks, many single-threaded algorithms have been proposed. However, graphs such as online social networks and knowledge

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Large Graph Mining - slideshare

2015-12-13  Graph Mining algorithms Let’s see what a graph mining algorithm looks like 35. A ranking algorithm Page Rank The web is a network of web pages In addition to the page content, the page linkage represents a useful source of knowledge and

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Large Scale Graph Mining with G-Miner - GitHub Pages

2021-5-11  workload of graph mining jobs also renders distributed com-puting a good option for large-scale graph mining; in con-trast, McSherry et al. showed that distributed vertex-centric systems have a high COST [3], i.e., the cost needed to out-perform a single-threaded implementation is high. Besides the much higher computational complexity, graph mining

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G-thinker: A Distributed Framework for Mining

2020-7-8  G-thinker: A Distributed Framework for Mining Subgraphs in a Big Graph Da Yan∗1, Guimu Guo∗2, Md Mashiur Rahman Chowdhury∗3, M. Tamer Ozsu¨ †4, Wei-Shinn Ku‡5, John C.S. Lui+6 ∗University of Alabama at Birmingham, {1yanda, 2guimuguo, 3mashiur}@uab †University of Waterloo, [email protected] ‡Auburn University, [email protected] +The Chinese

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A Review of Big Graph Mining Research - IOPscience

2017-3-14  Abstract. Big Graph Mining" is a continuously developing research that was started in 2009 until now. After 7 years, there are many researches that put this topic as the main concern. However, there is no mapping or summary concerning the important issues and solutions to explain this topic. This paper contains a summary of researches that have ...

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[PDF] Big graph mining for the web and social media ...

Graphs are everywhere: social networks, computer net- works, mobile call networks, the World Wide Web, protein interaction networks, and many more. The lower cost of disk storage, the success of social networking websites and Web 2.0 applications, and the high availability of data sources lead to graphs being generated at unprecedented size. They are now measured in terabytes or even petabytes ...

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Large Graph Mining: Power Tools and a Practitioner’s guide

2009-6-1  Web graph mining • How to order the importance of web pages? – Kleinberg’s algorithm HITS – PageRank – Tensor extension on HITS ( TOPHITS ) KDD '09 Faloutsos, Miller, Tsourakakis P5-27 CMU SCS Kleinberg’s Hubs and Authorities (the HITS method) Sparse adjacency matrix and its SVD: authority scores for 1 st topic

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BPGM: A big graph mining tool - TUP Journals

2014-2-7  Abstract: The design and implementation of a scalable parallel mining system target for big graph analysis has proven to be challenging. In this study, we propose a parallel data mining system for analyzing big graph data generated on a Bulk Synchronous Parallel (BSP) computing model named BSP-based Parallel Graph Mining (BPGM).

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Large Scale Graph Mining with G-Miner - GitHub Pages

2021-5-11  workload of graph mining jobs also renders distributed com-puting a good option for large-scale graph mining; in con-trast, McSherry et al. showed that distributed vertex-centric systems have a high COST [3], i.e., the cost needed to out-perform a single-threaded implementation is high. Besides the much higher computational complexity, graph mining

More

Large Graph Mining - slideshare

2015-12-13  Graph Mining algorithms Let’s see what a graph mining algorithm looks like 35. A ranking algorithm Page Rank The web is a network of web pages In addition to the page content, the page linkage represents a useful source of knowledge and

More

G-thinker: Big Graph Mining Made Easier and Faster -

G-thinker: Big Graph Mining Made Easier and Faster. This paper proposes a general system for compute-intensive graph mining tasks that find from a big graph all subgraphs that satisfy certain requirements (e.g., graph matching and community detection). Due to the broad range of applications of such tasks, many single-threaded algorithms have ...

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Graph Mining – Google Research

2021-6-17  As a fundamental tool in modeling and analyzing social, and information networks, large-scale graph mining is an important component of any tool set for big data analysis. Processing graphs with hundreds of billions of edges is only possible via developing distributed algorithms under distributed graph mining frameworks such as MapReduce ...

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GitHub - richardzhangrui/GraphMining: graph mining

graph mining on big data systems(course projcet of 15-799) - richardzhangrui/GraphMining

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G-thinker: A Distributed Framework for Mining

2020-7-8  G-thinker: A Distributed Framework for Mining Subgraphs in a Big Graph Da Yan∗1, Guimu Guo∗2, Md Mashiur Rahman Chowdhury∗3, M. Tamer Ozsu¨ †4, Wei-Shinn Ku‡5, John C.S. Lui+6 ∗University of Alabama at Birmingham, {1yanda, 2guimuguo, 3mashiur}@uab †University of Waterloo, [email protected] ‡Auburn University, [email protected] +The Chinese

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