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node2vec-c
node2vec implementation in dependency-less C++. node2vec uses short biased random walks to learn representations for vertices in unweighted graphs. Other implementations are available in C++ in SNAP project and a reference one in Python + Gensim.
This code was developed to be used in out paper, VERSE for a fair performance comparison. Moreover, the high-performance implementation in SNAP did not match the performance of the original implementation (as for 2017).
Installation and usage
For the executable:
make
should be enough on most platforms. If you need to change the default compiler (i.e. to Intel), use:
make CXX=icpc
IntelĀ® FMA availability is crucial for performance of the implementation, meaning the processor Haswell (2013). You will get a warning on runtime if your processor does not support it.
Usage
Usage: node2vec [OPTIONS]
Options:
-input PATH Input file in binary CSR format
-output PATH Output file, written in binary
-threads INT Number of threads to use (default 1)
Note: hyperthreading helps as well
-dim INT node2vec parameter d: dimensionality of
embeddings (default 128)
-nwalks INT node2vec parameter gamma: number of walks per
node (default 80)
-walklen INT node2vec parameter t: length of random walk
from each node(default 80)
-window INT node2vec parameter w: window size (default 10)
-nsamples INT node2vec parameter k: number of negative samples (default 5)
-p FLOAT node2vec parameter p: random walk bias (default 1)
-q FLOAT node2vec parameter q: random walk bias (default 1)
-lr FLOAT Initial learning rate
-seed INT Sets the random number generator seed to INT
-verbose INT Controls verbosity level in [0,1,2], 0 meaning
nothing will be displayed, and 2 mening
training progress will be displayed.
Graph format
This implementation uses a custom graph format, namely binary compressed sparse row (BCSR) format for efficiency and reduced memory usage. Converter for three common graph formats (MATLAB sparse matrix, adjacency list, edge list) can be found in the root directory of our main code repository.
Citing
If you find node2vec useful in your research, we ask that you cite the original paper:
@inproceedings{Grover:2016:NSF:2939672.2939754,
author = {Grover, Aditya and Leskovec, Jure},
title = {Node2Vec: Scalable Feature Learning for Networks},
booktitle = {Proceedings of the 22Nd ACM SIGKDD International Conference on Knowledge Discovery and Data Mining},
series = {KDD '16},
year = {2016},
isbn = {978-1-4503-4232-2},
location = {San Francisco, California, USA},
pages = {855--864},
numpages = {10},
url = {http://doi.acm.org/10.1145/2939672.2939754},
doi = {10.1145/2939672.2939754},
acmid = {2939754},
publisher = {ACM},
address = {New York, NY, USA},
keywords = {feature learning, graph representations, information networks, node embeddings},
}
Contact
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