Awesome

Rabbit Order

This is an implementation of the algorithm proposed in the following paper:
- J. Arai, H. Shiokawa, T. Yamamuro, M. Onizuka, and S. Iwamura. Rabbit Order: Just-in-time Parallel Reordering for Fast Graph Analysis. IEEE International Parallel and Distributed Processing Symposium (IPDPS), 2016.
Please read license.txt before reading or using the files.
Note that some graph datasets are already reordered, and so Rabbit Order will not show significant performance improvement on those graphs.
- For example, Laboratory for Web Algorithmics reorders graphs using the Layered Label Propagation algorithm.
- Web graphs are sometimes reordered by URL. This ordering is known to show good locality.

How to use

demo/reorder.cc is a sample program that reorders graphs by using Rabbit Order. Type make in the demo/ directory to build the program.

Requirements

g++ (4.9.2)
Boost C++ library (1.58.0)
libnuma (2.0.9)
libtcmalloc_minimal in google-perftools (2.1)

Numbers in each parenthesis are the oldest versions that we used to test this program.

Usage

Usage: reorder [-c] GRAPH_FILE
  -c    Print community IDs instead of a new ordering

If flag -c is given, this program runs in the clustering mode (described later); otherwise, it runs in the reordering mode.

Input

GRAPH_FILE has to be an edge-list file like the following:

Each line represents an edge. The first number is a source vertex ID, and the second number is a target vertex ID. Edges do not need to be sorted in some orderings, but vertex IDs should be zero-based contiguous numbers (i.e., 0, 1, 2, ...); otherwise, this demo program may consume more memory and show lower performance.

Many edge-list files in this format are found in [Stanford Large Network Dataset Collection] (http://snap.stanford.edu/data/index.html).

Output (reordering mode)

The program prints a new ordering as follows:

8
16
1
4
...

These lines represent the following permutation:

Vertex 0 ==> Vertex 8
Vertex 1 ==> Vertex 16
Vertex 2 ==> Vertex 1
Vertex 3 ==> Vertex 4
...

Output (clustering mode)

The program prints a clustering result as follows:

These lines represent the following classification:

Vertex 0 ==> Cluster 1
Vertex 1 ==> Cluster 5
Vertex 2 ==> Cluster 1
Vertex 3 ==> Cluster 5
Vertex 4 ==> Cluster 5
Vertex 5 ==> Cluster 9
...

Note that the cluster IDs may be non-contiguous.