greed2411

//===================================================================
// File: circular_buffer.cpp
//
// Desc: A Circular Buffer implementation in C++.
//
// Copyright © 2019 Edwin Cloud. All rights reserved.
//
//===================================================================

//-------------------------------------------------------------------

greed2411

//===================================================================
// File: circular_buffer.cpp
//
// Desc: A Circular Buffer implementation in C++.
//
// Copyright © 2019 Edwin Cloud. All rights reserved.
//
//===================================================================

//-------------------------------------------------------------------

greed2411

Updated 4/11/2018

Here's my experience of installing the NVIDIA CUDA kit 9.0 on a fresh install of Ubuntu Desktop 16.04.4 LTS.

Table of Contents

• Install NVIDIA Graphics Driver via apt-get
• Install CUDA
• Install cuDNN

Table of contents generated with markdown-toc

greed2411

A note on implementing community detection using Apache Spark + GraphX

Girvan Newman Algorithm

This is one of the earliest methods of community detection. This method is simple to understand and can be easily distributed across clusters for faster processing. Key assumption is that the graph is undirected and unweighted. But it is not hard to extend to directed graphs + weighted edges.

The algorithm is fairly straightforward. It defines a new measure called edge betweenness centrality based on which a divisive hierarchical clustering algorithm is designed to find communities. The stopping criteria for this uses a popular metric called modularity which quantifies how cohesive the communities are during the clustering process.

Side note: A bit of search reveled no implementation of this algorithm in a distributed way (mainly because its slow and better algorithms are available?). So, this note would pave way to use this naive algorithm inspite of its high time complexity.