Python bindings for the high‑performance Rust graph/network embedding library graphembed
This crate provides an executable and a library for embedding of directed or undirected graphs with positively weighted edges. We engineered and optimized current network embedding algorithms for large-scale network embedding, especially biological network. This crate was developed by Jianshu Zhao and Jean-Pierre Both jpboth. We have a copy here in Github
wget https://gitlab.com/-/project/64961144/uploads/9d7d0b038140cb67c584f01cd6dafac9/graphembed_Linux_x86-64_v0.1.6.zip
unzip graphembed_Linux_x86-64_v0.1.6.zip
chmod a+x ./graphembed
./graphembed -h
conda install -c conda-forge -c bioconda graphembed
brew tap jianshu93/graphembed
brew update
brew install graphembed
pip install graphembed_rs
### or you can build from source (Linux) after installing maturin
git clone https://gitlab.com/Jianshu_Zhao/graphembed
cd graphembed
pip install maturin
### note: for macOS, you need to change the line "features = ["pyo3/extension-module", "intel-mkl-static", "simdeez_f"]" in pyporject.toml to "features = ["pyo3/extension-module","openblas-system","stdsimd"]", you also need to Install OpenBLAS and add to system library path via Homebrew
maturin develop --release
#### Prepare some data
wget https://gitlab.com/-/project/64961144/uploads/4e341383d62d86d1dd66e668e91b2c07/BlogCatalog.txt
import os
os.environ["RUST_LOG"] = "info"
import graphembed_rs.graphembed_rs as ge
import graphembed_rs.load_utils as ge_utils
help(ge)
help(ge_utils)
### HOPE
ge.embed_hope_rank("BlogCatalog.txt", target_rank=128, nbiter=4,output="embedding_output")
out_vectors=ge_utils.load_embedding_bson("embedding_output.bson")
print("OUT embedding shape :", out_vectors.shape)
print("first OUT vector :", out_vectors[0])
### Sketching
### sketching only
ge.embed_sketching("BlogCatalog.txt", decay=0.3, dim=128, nbiter=5, symetric=True, output="embedding_output")
out_vectors=ge_utils.load_embedding_bson("embedding_output.bson")
print("OUT embedding shape :", out_vectors.shape)
print("first OUT vector :", out_vectors[0])
### validate accuracy
auc_scores = ge.validate_sketching("BlogCatalog.txt",decay=0.3, dim=128, nbiter=3, nbpass=1, skip_frac=0.2,symetric=True, centric=True)
print("Standard AUC per pass:", auc_scores)
NodeSketch : Highly-Efficient Graph Embeddings via Recursive Sketching KDD 2019. see nodesketch
D. Yang,P. Rosso,Bin-Li, P. Cudre-Mauroux.
It is based on multi hop neighbourhood identification via sensitive hashing based on the recent algorithm probminhash. See arxiv or ieee-2022.
The algorithm associates a probability distribution on neighbours of each point depending on edge weights and distance to the point.
Then this distribution is hashed to build a (discrete) embedding vector consisting in nodes identifiers.
The distance between embedded vectors is the Jaccard distance so we get
a real distance on the embedding space for the symetric embedding.
An extension of the paper is also implemented to get asymetric embedding for directed graph. The similarity is also based on the hash of sets (nodes going to or from) a given node but then the dissimilarity is no more a distance (no symetry and some discrepancy with the triangular inequality).
The orkut graph with 3 millions nodes and 100 millions of edge is embedded in 5' with a 24 core i9 laptop with this algorithm giving an AUC of 0.95.
Asymetric Transitivity Preserving Graph Embedding 2016. M. Ou, P Cui, J. Pei, Z. Zhang and W. Zhu. See hope.
The objective is to provide an asymetric graph embedding and get estimate of the precision of the embedding in function of its dimension.
We use the Adamic-Adar matricial representation of the graph. (It must be noted that the ponderation of a node by the number of couples joined by it is called Resource Allocation in the Graph Kernel litterature).
The asymetric embedding is obtained from the left and right singular eigenvectors of the Adamic-Adar representation of the graph.
Source node are related to left singular vectors and target nodes to the right ones.
The similarity measure is the dot product, so it is not a norm.
The svd is approximated by randomization as described in Halko-Tropp 2011 as implemented in the annembed crate.
Validation of embeddings is assessed via standard Auc with random deletion of edges. See documentation in the link module and embed binary. We give also a variation based on centric quality assessment as explained at cauc
Small datasets are given in the Data subdirectory (with 7z compression) to run tests.
Larger datasets can be downloaded from the SNAP data collections https://snap.stanford.edu/data
Symetric graphs
Asymetric graphs
wiki-vote https://snap.stanford.edu/data/wiki-Vote.html 7115 nodes 103689 edges
Cora : http://konect.cc/networks/subelj_cora citation network 23166 nodes 91500 edges
These graphs were used in results see below.
Beware of the possible need to convert from Windows to Linux End Of Line, see the dos2unix utility.
Possibly some data can need to be converted from Tsv format to Csv, before being read by the program.
Symetric
Asymetric
Embedding and link prediction evaluation for the above data sets are given in file resultats.md A more global analysis of the embedding with the nodesketch module is done for the orkut graph in file orkut.md
A preliminary of node centric quality estimation is provided in the validation module (see documentation in validation::link).
For the embedding using the randomized svd, increasing the embedding dimension is interesting as far as the corresponding eigenvalues continue to decrease significantly.
The munmun_twitter_social graph shows that treating a directed graph as an undirected graph give significantly different results in terms of link prediction AUC.
An implementation of Generalized Svd comes as a by-product in module gsvd.
The crate provides features (with a default configuration), required by the annembed dependency, to specify which version of lapack you want to use or the choice of simd implementation.
The embed module can be generated with the standard : cargo doc --no-deps --bin embed.
The Hope embedding relying on matrices computations limits the size of the graph to some hundred thousands nodes. It is intrinsically asymetric in nature. It nevertheless gives access to the spectrum of Adamic Adar matrix representing the graph and so to the required dimension to get a valid embedding in $R^{n}$.
The Sketching embedding is much faster for large graphs but embeds in a space consisting in sequences of node id equipped with the Jaccard distance. It is particularly efficient in low degrees graph.
The embed module takes embedding and possibly validation commands (link prediction task) in one directive.
The general syntax is :
graphembed file_description [validation_command --validation_arguments] sketching mode --embedding_arguments
for example:
For a symetric graph we get:
just embedding: graphembed --csv ./Data/Graphs/Orkut/com-orkut.ungraph.txt --symetric sketching --decay 0.2 --dim 200 --nbiter
embedding and validation:
graphembed --csv ./Data/Graphs/Orkut/com-orkut.ungraph.txt --symetric validation --nbpass 5 --skip 0.15 sketching --decay 0.2 --dim 200 --nbiter 5
For an asymetric graph we get
graphembed --csv ./Data/Graphs/asymetric.csv validation --nbpass 5 --skip 0.15 sketching --decay 0.2 --dim 200 --nbiter 5
More details can be found in docs of the embed module. Use cargo doc --no-dep --bin embed (and cargo doc --no-dep) as usual.
Licensed under either of
at your option.
FAQs
Python bindings for the high‑performance Rust graph/network embedding library graphembed
We found that graphembed-rs demonstrated a healthy version release cadence and project activity because the last version was released less than a year ago. It has 1 open source maintainer collaborating on the project.
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