Probabilistic reasoning in intelligent systems: networks of plausible inference
Probabilistic reasoning in intelligent systems: networks of plausible inference
Introduction to algorithms
SOSP '03 Proceedings of the nineteenth ACM symposium on Operating systems principles
Fast Random Walk with Restart and Its Applications
ICDM '06 Proceedings of the Sixth International Conference on Data Mining
MapReduce: simplified data processing on large clusters
OSDI'04 Proceedings of the 6th conference on Symposium on Opearting Systems Design & Implementation - Volume 6
MapReduce: simplified data processing on large clusters
Communications of the ACM - 50th anniversary issue: 1958 - 2008
Graph Twiddling in a MapReduce World
Computing in Science and Engineering
Data-intensive text processing with MapReduce
NAACL-Tutorials '09 Proceedings of Human Language Technologies: The 2009 Annual Conference of the North American Chapter of the Association for Computational Linguistics, Companion Volume: Tutorial Abstracts
PEGASUS: A Peta-Scale Graph Mining System Implementation and Observations
ICDM '09 Proceedings of the 2009 Ninth IEEE International Conference on Data Mining
What is Twitter, a social network or a news media?
Proceedings of the 19th international conference on World wide web
Pregel: a system for large-scale graph processing
Proceedings of the 2010 ACM SIGMOD International Conference on Management of data
Realistic, mathematically tractable graph generation and evolution, using kronecker multiplication
PKDD'05 Proceedings of the 9th European conference on Principles and Practice of Knowledge Discovery in Databases
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Graph mining is a popular technique for discovering the hidden structures or important instances in a graph, but the computational efficiency is usually a cause for concern when dealing with large-scale graphs containing billions of entities. Cloud computing is widely regarded as a feasible solution to the problem. In this work, we present an open source graph mining library called the MapReduce Graph Mining Framework (MGMF) to be a robust and efficient MapReduce-based graph mining tool. We start from dividing graph mining algorithms into four categories and designing a MapReduce framework for algorithms in each category. The experimental results show that MGMF is 3 to 20 times more efficient than PEGASUS, a state-of-the-art library for graph mining on MapReduce. Moreover, it provides better coverage of different graph mining algorithms. We also validate our framework on billion-scaled networks to demonstrate that it is scalable to the number of machines. Fur-thermore, we test and compare the feasibility between single ma-chine and the cloud computing technique. The effects of different file input formats for MapReduce are investigated as well. Our implemented open-source library can be downloaded from http://mslab.csie.ntu.edu.tw/~noahsark/MGMF/.