A Parallel Graphlet Decomposition Library for Large Graphs
Switch branches/tags
Nothing to show
Clone or download
Fetching latest commit…
Cannot retrieve the latest commit at this time.

README.md

Parallel Parameterized Graphlet Decomposition (PGD) Library

A fast parallel graphlet decomposition library for large graphs.

Please refer to our paper Efficient Graphlet Counting for Large Networks for detailed discussion on the algorithm.

Synopsis

In short, a parameterized high performance library for counting motifs in large sparse graphs.

Setup

First, you'll need to compile PGD.

    $ cd path/to/pgd/
    $ make

Afterwards, the following should work:

    # compute the motif counts
    ./pgd -f sample_graph.csv

Currently, PGD supports only UNIX-based systems. PGD has been tested on Ubuntu linux (10.10 tested) and Mac OSX (Lion tested) with gcc-mp-4.7 and gcc-mp-4.5.4

Please let us know if you run into any issues.

Motif Symbol Description Comp. ρ δ davg r T K χ D B C
g41 4-clique 1.00 3 3.0 1.00 4 3 4 1 0 1
g42 4-chordal-cycle 0.83 3 2.5 -0.66 2 2 3 2 1 1
g43 4-tailed-triangle 0.67 3 2.0 -0.71 1 2 3 2 2 1
g44 4-cycle 0.67 2 2.0 1.00 0 2 2 2 1 1
g45 3-star 0.50 3 1.5 -1.00 0 1 2 2 3 1
g46 4-path 0.50 2 1.5 -0.50 0 1 2 3 2 1
g47 4-node-1-triangle 0.50 2 1.5 1.00 1 2 3 1 0 2
g48 4-node-2-star 0.33 2 1.0 -1.00 0 1 2 2 1 2
g49 4-node-2-edge 0.33 1 1.0 1.00 0 1 2 1 0 2
g410 4-node-1-edge 0.17 1 0.5 1.00 0 1 2 1 0 3
g411 4-node-independent 0.00 0 0.0 0.00 0 0 1 0 4
g31 triangle 1.00 2 2.0 1.00 1 2 3 1 0 1
g32 2-star 0.67 2 1.33 -1.00 0 1 2 2 1 1
g33 3-node-1-edge 0.33 1 0.67 1.00 0 1 2 1 0 2
g34 3-node-independent 0.00 0 0.00 0.00 0 0 1 0 3
g21 edge 1.00 1 1.0 1.00 0 1 2 1 0 1
g22 2-node-independent 0.00 0 0.0 0.00 0 0 1 0 2

Input file format

Note comments are denoted by %. First line represents n n m where n is the number of nodes and m is the number of edges |E|. For instance, the first line above is 4 4 6 and indicates the number of nodes is n=4 and number of edges is m=6.

A graph file with the extension .mtx is read using this strict mtx graph reader. Thus, if the graph file does not strictly follow the above mtx format, then the file extension should be changed to allow it to be read by the more flexible graph reader discussed below.

  • Edge list: These codes are designed to be as flexible as possible and accept many variations of edge lists. Note these codes may be slightly slower than the mtx reader. This is due to allowing flexible edge list formats. Hence, this reader must perform many checks to figure out the exact format of the input file, and performs any necessary preprocessing work that may be required.

    • Delimiters: The mcpack reader accepts comma, space, or tab delimited edge lists.

    • Comments: Comments are allowed and should be denoted with the first character of a newline as # or %

    • Weights: If an edge list contains weights on the 3rd column, they are simply ignored. A user may specify to read the weights by setting the wt parameter or by noting the graph is in fact a temporal graph.

    • Multigraph: When an edge list contains multiple edges, we simply remove the duplicate edges.

    • The edge list may also contain gaps in the vertex ids (non sequential vertex ids) and start from any positive integer. Self-loops are removed.

    • The edge list is assumed to be undirected. However, if a directed graph is given, it is simply treated as undirected.

Output Graphlet Quantities

The PGD family of graphlet decomposition algorithms provide three types of output 1. Global macro statistics indicating the total frequency of each motif 2. Local micro statistics representing the number of times each motif appears (for each edge or node in the graph) 3. Graphlet frequency distribution

NOTE: The total counts for each motif is outputted to the screen by default.

Macro motif counts

The macro (global) graphlet counts are printed to the screen by default. These statistics may also be saved to a file using --macro filename.macro where filename.macro is the path to save stats.

    ./pgd -f sample_graph.csv --macro sample_graph.macro

Micro motif counts

The motif counts for each edge may also be saved using the --micro filename.micro.

    ./pgd -f sample_graph.csv --micro sample_graph.micro

Graphlet frequency distribution (GFD)

To output the graphlet frequency distribution, use the --gfd filename.gfd option.

    ./pgd -f sample_graph.csv --gfd sample_graph.gfd

Advanced

Orderings

The PGD algorithms are easily adapted to use various ordering strategies. To prescribe an ordering, use the -o option with one of the following:

Ordering techniques Description
deg order by degree
kcore degeneracy order
dual_deg ordering defined by the sum of degrees from neighbors
dual_kcore order by the sum of core numbers from neighbors
kcore_deg order by the weight k(v)d(v)
rand uniformly random order
natural use the order given as input

Direction of ordering

Descending order is the default (largest to smallest). For ascending order (smallest to largest), simply set --s2l as follows:

./pgd -f sample_graph.csv --s2l

To reverse the order of the neighbors (for each node) use `--s2l_neigh``.

Command Line Interface (CLI)

You can execute PGD with --help to see the list of options

$ ./pgd --help

=================================================================================
Parallel Parameterized Graphlet Decomposition (PGD) Library
=================================================================================
-f, --file,--graph              : Input GRAPH file for computing the graphlets (e.g., matrix market format, simple edge list). 
-a, --algorithm                 : Algorithm for the GRAPHLET DECOMPOSITION. Default: exact, approximate, etc.
---------------------------------------------------------------------------------
-w, --workers                   : Number of WORKERS (processing units) for the algorithm to use (default = max). 
-b, --block_size                : Size of blocks assigned to workers (processing units), that is, 1, 64, 512, etc.  Default: -b 64
---------------------------------------------------------------------------------
-o, --ordering                  : Strategy used to determine the order in which the edge/node graphlets are computed.
                                  Default: -o degree (kcore, rand, natural/off, etc.).
    --s2l                       : Direction of the ordering (default: largest to smallest).
                                  Note to order from smallest to largest, set '--s2l'  
-n, --neigh_ordering            : Ordering to use for the neighbors of each node. 
                    	           Default: degree (kcore, rand, natural, etc.)
                                   Note only applicable if CSC/CSR is used (-r csc).
    --s2l_neigh                 : Order direction for neighbor/csc ordering strategy
                                  (e.g., --neigh_ordering degree --s2l_neigh, default: largest to smallest)
---------------------------------------------------------------------------------
-c, --counts,--macro            : Compute MACRO (GLOBAL) GRAPHLET FEATURES and save counts to file (e.g., --counts name.graphlets)
-m, --micro                     : Compute MICRO (LOCAL) GRAPHLET FEATURES and save EDGE-by-MOTIF feature matrix (-m name.micro_graphlets)
                                  Default: OFF. NOTE: Turn ON edge graphlet counting by specifying an output file, e.g., '-m name.micro_graphlets' 
---------------------------------------------------------------------------------
-v, --verbose                   : Output additional details to the screen. 
-?, -h, --help                  : Print out this help menu. 


REPRESENTATION: Example: ./pgd -r csc (adj, etc.)
=================================================================================
-r,   --rep                     : Graph representation [adj, csc, hybrid, auto, etc].
                                  Default: Auto select optimal. 
   'adj'    - adjacency matrix  : dense n by n matrix, O(|V|^2) storage cost
   'csc'    - comp. sparse col  : large sparse graphs, O(|V|+|E|) storage cost
   'hybrid' -  csc + adj        : small sparse and dense graphs, O(|V|^2 + |V| + |E|) storage cost
-l, --adj_limit                 : Threshold/limit for creating adj representation. Default: '-l 10000' (that is <10000 nodes).


ORDERING TECHNIQUES: Example: ./pgd -o degree (kcore, rand, etc.)
=================================================================================
'degree',   'deg'                    : O(|V|)
'kcore',                             : O(|E|)
'rand', 'random'                     : O(|V|)
'off',  'natural'                    

 Other methods for ordering include: 
'kcore_degree', 'kcore_deg'          : O(|V|)
'degree_vol',   'deg_vol'            : O(|E|)
'kcore_vol',                         : O(|E|)
'deg_kcore_vol'                      : O(|E|)
------------------------------------------------------------------
NOTE: Default ordering is kcore (degeneracy order). For natural order, use '-o off' or '-o natural'

API and Sample Codes

Exact Sample Codes

Sample codes for computing exact graphlet statistics using the family of exact graphlet decomposition algorithms from pgd library.

  • macro graphlet statistics
  • micro graphlet statistics

Let us note that if the micro-level graphlet statistics are not needed, then it is recommended to use the macro graphlet decomposition algorithms. These are highly optimized for this task and thus are significantly more space-efficient while also faster and more scalable.

Macro graphlet statistics

Compute the global frequency of the various motif patterns with just two lines:

// read graph, optimize alg/data structs, etc.
graphlet_core G("sample_graph.csv");
G.graphlet_decomposition();

Afterwards, it is easy to print or write global motif counts to a file.

G.print_graphlet_counts(); // print to screen

or SAVE to a file,

G.write_macro_stats(path);

Micro graphlet statistics

Compute the frequency of the various motif patterns with just two lines:

// read graph, optimize alg/data structs, etc.
graphlet_core G("sample_graph.csv");
G.graphlet_decomposition_micro();

Afterwards, it is easy to print or write the motif counts to a file.

G.print_micro_stats(); // print to screen

or SAVE to a file,

G.write_micro_stats(path);

GFD Sample Codes

Estimate GFD

To obtain a fast and accurate estimation of the graphlet frequency distribution, use the following:

// Approximate GFD by sampling uniformly at random 10% of the edges (vertices) to use
G.graphlet_approximation(0.10);

Afterwards, the GFD can be approximated from these counts as follows:

// Estimate graphlet distribution for connected and disconnected motifs
G.compute_GFD();
Distribution API Call
Graphlet Freq. Distribution (GFD) compute_GFD()
Connected GFD compute_connected_GFD()
Disconnected GFD compute_disconnected_GFD()

Exact GFD

Exact graphlet distributions may also be computed fast by simply using an exact graphlet decomposition method from those expressed by the pgd library and then using the API calls above in the table.

G.graphlet_decomposition();
G.compute_GFD();

Documentation

The documentation is generated (using doxygen) by simply typing make doc in the root directory of pgd.

make doc

This creates the ./doc directory with the documentation. To update the documentation, use make cleandoc then make doc.

Doxygen and graphvis are required and installed via homebrew (if not installed already). Currently, this works only for Mac OSX and other Unix-based systems.

Additional Info.

To generate the documentation you must have doxygen and graphviz installed. On Mac OSX these can be stalled using homebrew with the following commands:

# install doxygen and graphviz using homebrew on Mac OSX
brew install doxygen
brew install graphviz

Afterwards, the documentation is generated by simply typing make docs in the root directory of pgd. This creates the ./docs directory with the documentation.

Terms and conditions

Please feel free to use the PGD library. We only ask that you cite:

  1. Nesreen K. Ahmed, Jennifer Neville, Ryan A. Rossi, Nick Duffield, Efficient Graphlet Counting for Large Networks, IEEE International Conference on Data Mining (ICDM), pages 10, 2015.

    Also the BiBTeX for [1] is:

     @inproceedings{ahmed2015icdm,
         title={Efficient Graphlet Counting for Large Networks},
         author={Nesreen K. Ahmed and Jennifer Neville and Ryan A. Rossi and Nick Duffield},
         booktitle={ICDM},
         pages={1--10},
         year={2015}
     }
    
  2. Nesreen K. Ahmed, Jennifer Neville, Ryan A. Rossi, Nick Duffield Fast Parallel Graphlet Counting for Large Networks, arXiv preprint 1506.04322, 2015.

Graph Datasets

Please check the following link for additional graph datasets: [Network Repository] (http://networkrepository.com/)

See the LICENSE file for further information. Copyright 2012-2015, Nesreen K. Ahmed. All rights reserved.