Network (graph, dataset) converter from Pajek, Metis and .nsl formats (including .ncol, Stanford SNAP and Edge/Arcs Graph) to .nsl (.nse/a that are more common than .snap and .ncol) and .rcg (Readable Compact Graph, former .hig; used by DAOC / HiReCS libs) formats. Additionally, an adjacency matrix conversion from the Mathlab (.mat) format to .nsl is provided by the dedicated script (matToNsl).
\author: Artem Lutov artem@exascale.info
(c) RCG (Readable Compact Graph)
- pajek network
- metis graph (network)
- nse - nodes are specified in lines consisting of the single Space/tab separated, possibly weighted Edge (undirected link, i.e. either AB or BA is specified):
<src_id> <dst_id> [<weight>]
with#comments and selflinks, without backward direction specification.
The same as Stanford SNAP network format
Also known as [Weighted] Edge Graph
Optionally reduced to the ncol format - nsa - nodes are specified in lines consisting of the single Space/tab separated, possibly weighted Arc (directed link):
<src_id> <dst_id> <weight>
with#comments, selflinks and backward direction specification that is a [Weighted] Arcs Graph, a generalization of the LFR Benchmark generated networks
Mathlab (.mat) adjacency matrix conversion to the nsl (nse/nsa) only format is performed by the
matToNsl.pyscript.
- rcg format (former hig)
- nsl (stands for nse/nsa and includes SNAP, ncol and Edge/Arcs Graph)
- snap format is nse with removed weights (use
--unweight -o nseoptions) - ncol format is snap without the comments (use
--unweight --nocoms -o nseoptions)
- snap format is nse with removed weights (use
The converter is written for the Python3 considering backward compatibility with Pyhon2 and PyPy. It is tested on Python3, but should also run on Python2 and PyPy.
There no any external dependencies.
The converter is implemented as a serial parser, i.e. it can process files of any size having very small memory footprint (until the --remdup option is specified to remove the duplicated links).
Just run the converter with specified input and output formats. Some formats are automatically recognized by the file extension.
$ ./convert.py tmp/karate.graph -i mts
File "tmp/karate.graph" is opened, converting...
unweight: False
remdub: False
frcedg: False
inpfmt: mts
resolve: o
outfmt: rcg
commented: True
File tmp/karate.rcg is created, filling...
Metis format weighted: False, selfweights: 0
Parsed weighted: False, newsection: True
tmp/karate.graph -> tmp/karate.rcg conversion is completed
$ ./convert.py -h
usage: convert.py [-h] [-f] [-i {pjk,nsa,nse,mts}] [-d] [-e] [-u] [-c]
[-o {nsa,nse,rcg}] [-r {o,r,s}]
[network]
Convert format of the specified network (graph).
positional arguments:
network the network (graph) to be converted
optional arguments:
-h, --help show this help message and exit
-f, --showfmt show supporting I/O formats description and exit
Input Format:
-i {pjk,nsa,nse,mts}, --inpfmt {pjk,nsa,nse,mts}
input network (graph) format
Additional Modifiers:
-d, --remdup remove duplicated links to have unique ones
-e, --frcedg force edges output even in case of ars input: the
output edge is created by the first occurrence of the
input link (edge/arc) and has weight of this link
omitting the subsequent back link (if exists)
-u, --unweight force links to be unweighted instead of having the
input weights
-c, --nocoms clear (avoid) comments in the output file (conversion
provenance is not added, headers for .nsX are omitted,
etc.). Can be useful when .ncol file should be
produces instead of the Stanford SNAP-like format
Output Format:
-o {nsa,nse,rcg}, --outfmt {nsa,nse,rcg}
output format for the network (graph)
-r {o,r,s}, --resolve {o,r,s}
resolution strategy in case the output file is already
exists: o - overwrite the output file, r - rename the
existing output file and create the new one, s - skip
processing if such output file already exists
$ ./matToNsl.py -h
usage: matToNsl.py [-h] [-d] MatNet [MatNet ...]
Network converter from mathlab format to .nsl (nse/nsa).
positional arguments:
MatNet unsigned input network in the .mat format
optional arguments:
-h, --help show this help message and exit
-d, --directed form directed output network from possibly directed input
network
- Networks form the 10th DIMACS'13 competition in Metis format with ground-truth modularity
- Networks from Standford SNAP (unweinghted nse format) with ground-truth clusters
- LFR Benchmark to produce synthetic networks in nsa format with ground-truth clusters
Rcg (OUTP) - Readable Compact Graph format (former hig), native input format of DAOC. This format is similar to Pajek, but ids can start from any non-negative number and might not form a solid range. RCG is a readable and compact network format suitable for the evolving networks.. File extensions: rcg, hig
Mts (INP) - Metis Graph (Network) format. File extensions: graph, mtg, met. Specification:
% Comments start with '%' symbol
% Header:
<vertices_num> <endges_num> [<format_bin> [vwnum]]
% Body, vertices_num lines without the comments:
[vsize] [vweight_1 .. vweight_vwnum] vid1 [eweight1] vid2 [eweight2] ...
...
Notations:
Header:
-
ertices_num- the number of vertices in the network (graph) -
endges_num- the number of edges (not directed, A-B and B-A counted as a single edge)ATTENTION: The edges are counted only once, but specified in each direction. The arcs must exist in both directions and their weights are symmetric, i.e. edges.
-
format_bin- up to3digits{0, 1}:<vsized><vweighted><eweighted>vsized- whether the size of each vertex is specified (vsize)vweighted- whether the vertex weights are specified (vweight_1 .. vweight_vmnum)eweighted- whether edges weights are specifiedeweight<i>
ATTENTION: When the fmt parameter is not provided, it is assumed that the vertex sizes, vertex WEIGHTS, and edge weights are all equal to 1 and NOT present in the file.
-
vm_num- the number of weights in each vertex (not the number of edges)
Body:
vsize- size of the vertex, integer >= 0. NOTE: do not used normallyvweight- weight the vertex, integer >= 0vid- vertex id, integer >= 1. ATTENTION: can't be equal to 0eweight- edge weight, integer >= 1
Pjk (INP) - Pajek Network format. Node ids started with 1, both [weighted] arcs and edges might be present.. File extensions: pjk, pajek, net, pjn
Nsl - nodes graph specified by the newline separated links (edges/arcs), which are optionally weighted.
Nse (INP, OUTP) - nodes are specified in lines consisting of the single Space/tab separated, possibly weighted Edge (undirected link). It is similar to the ncol format and [Weighted] Edge Graph, but self-edges are allowed to represent node weights and the line comment is allowed using # symbol.. File extensions: nse, snap, ncol. Specification:
# Comments start with '#', the header is optional:
# Nodes: <nodes_num> Edges: <edges_num>
<from_id> <to_id> [<weight>]
...
Notations:
The header is optional. The edges (undirected links) are unique, i.e. either AB or BA is specified.
Id is a positive integer number (>= 1), id range is solid.
Weight is a non-negative floating point number.
Nsa (INP, OUTP) - nodes are specified in lines consisting of the single Space/tab separated, possibly weighted Arc (directed link), a self-arc can be used to represent the node weight and the line comment is allowed using # symbol.. File extensions: nsa. Specification:
# Comments start with '#', the header is optional:
# Nodes: <nodes_num> Arcs: <arcs_num>
<from_id> <to_id> [<weight>]
...
Notations:
The header is optional. The arcs (directed links) are unique and always in pairs, i.e. BA should be specified until it's weight is zero if AB is specified.
Id is a positive integer number (>= 1), id range is solid.
Weight is a non-negative floating point number.
- Clubmark - A parallel isolation framework for benchmarking and profiling clustering (community detection) algorithms considering overlaps (covers).
- LFR Benchmark for Undirected Weighted Overlapping networks - generates synthetic networks in nsa format with ground-truth clustering to evaluate clustering algorithms.
Note: Please, star this project if you use it.