Graph_algorithm/OpenANE
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update_v1.0

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Chengbin 2020-05-28 13:25:16 +08:00
parent c7f06f54e5
commit b1203d9f25
2 changed files with 1 additions and 47 deletions
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45
requirements.txt
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@ -7,7 +7,6 @@ networkx==2.3
gensim==3.7.3 gensim==3.7.3
scikit-learn==0.19.0 # to do... compatible with >0.20 scikit-learn==0.19.0 # to do... compatible with >0.20
pandas==0.23.0 pandas==0.23.0
psutil==5.6.3
# Enable GPU: # Enable GPU:
# If using anaconda, run `conda install tensorflow-gpu==1.10.0` # If using anaconda, run `conda install tensorflow-gpu==1.10.0`
@ -17,47 +16,3 @@ psutil==5.6.3
# Or simply build from docker image: docker pull tensorflow/tensorflow:1.10.0-gpu-py3 # Or simply build from docker image: docker pull tensorflow/tensorflow:1.10.0-gpu-py3
# ref: https://www.tensorflow.org/install/docker#gpu_support # ref: https://www.tensorflow.org/install/docker#gpu_support
'''
Package Version
--------------- --------
absl-py 0.7.1
astor 0.8.0
boto 2.49.0
boto3 1.9.160
botocore 1.12.160
certifi 2019.3.9
chardet 3.0.4
decorator 4.4.0
docutils 0.14
gast 0.2.2
gensim 3.7.3
grpcio 1.21.1
idna 2.8
jmespath 0.9.4
Markdown 3.1.1
mkl-fft 1.0.12
mkl-random 1.0.2
networkx 2.3
numpy 1.14.5
pandas 0.23.0
pip 19.1.1
protobuf 3.8.0
psutil 5.6.3
python-dateutil 2.8.0
pytz 2019.1
requests 2.22.0
s3transfer 0.2.0
scikit-learn 0.19.0
scipy 1.1.0
setuptools 39.1.0
six 1.12.0
smart-open 1.8.4
tensorboard 1.10.0
tensorflow 1.10.0
termcolor 1.1.0
urllib3 1.25.3
Werkzeug 0.15.4
wheel 0.33.4
'''

3
src/libnrl/abrw.py
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@ -96,8 +96,7 @@ class ABRW(object):
# n*n*8 is the bytes required by pairwise similarity matrix; 2e9 = 2GB ROM remained for safety reason # n*n*8 is the bytes required by pairwise similarity matrix; 2e9 = 2GB ROM remained for safety reason
# if your computer have 200G memory, there should be no problem for graph with 100k nodes # if your computer have 200G memory, there should be no problem for graph with 100k nodes
# this naive implementation is **faster** than BallTree implementation, thanks to numpy # this naive implementation is **faster** than BallTree implementation, thanks to numpy
#if n*n*8 + n*n*8 + n*5000*8 + 2e9 < free_memory and n < 1e5: # X_sim[n,n] dense + A[n,n] if dense + X[n,5000] if dense with max 5000 feats + 2e9 for safety if False: # X_sim[n,n] dense + A[n,n] if dense + X[n,5000] if dense with max 5000 feats + 2e9 for safety
if False:
print('naive implementation + intro-select ') print('naive implementation + intro-select ')
t1 = time.time() t1 = time.time()
X_sim = pairwise_similarity(X.todense()) X_sim = pairwise_similarity(X.todense())