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An efficient subgraph-centric distributed graph computing system for processing large-scale power-law graphs

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DRONE

Install and run

A Go implementation of Distributed Graph Computations.
The communication library is based on grpc, so you should run the following commands for installing grpc

go get -v -u "github.com/gogo/protobuf/{proto,protoc-gen-gogo,gogoproto,protoc-gen-gofast}"

In this project, there are two branches. The 'master' branch is implemented as edge cut and 'vertexcut' branch is a vertex-cut implementation.
For running the project, you can work as

cd src
go run master.go &
go run worker*.go id total_workers

The id is start from 1 and the total_workers is the number of workers you want.
For example:

cd src
go run master.go &
go run worker1.go 1 2
go run worker1.go 2 2

The test_data/config.txt is the config file for master as slaves.
The first line indicates the master's ip:port, and the following lines corresponds to the workers'.

Brief Introduction

We introduce our new graph-parallel model: subgraph-centric programming and vertex-cut partitioning heterogeneous model (SVHM).
Briefly, SVHM opens up the entire subgraph of each partition to the user-function, retaining the inherent advantages of the subgraph-centric model such as less communication overhead and faster convergency. By integrating the \emph{vertex-cut} partitioning scheme into the model, SVHM is able to process the large-scale power-law graphs efficiently.
For more details, you can reference here

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An efficient subgraph-centric distributed graph computing system for processing large-scale power-law graphs

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