baldassarrefe / graph-network-explainability

Explainability techniques for Graph Networks, applied to a synthetic dataset and an organic chemistry task. Code for the workshop paper "Explainability Techniques for Graph Convolutional Networks" (ICML19)

Hi, I'm Federico 👋

Postdoc at Meta AI in Paris

My research focuses on the explainability of deep learning, improving our understanding of modern learning systems from their internal representations to the way they determine a prediction.

Learn more about me and my research on my website.

Connect with me:

• Twitter
• LinkedIn
• StackOverflow

 

graph-network-explainability's Issues

problems I met when I was running the code for solubility

Hi! I met some problems when I was running the predict part of code for solubility. I'm wondering how can I find the datasets for this part, because it seems that some ".yaml" files are absent?

Unable to setup the conda env & not able to install torchgraph

Hi,

The conda env installation/ creation is failing:
conda env create -n gn-exp -f conda.yaml
Collecting package metadata (repodata.json): done
Solving environment: failed

ResolvePackageNotFound:

• fontconfig==2.13.0=h9420a91_0
• zstd==1.3.7=h0b5b093_0
• glib==2.56.2=hd408876_0
• libgcc-ng==8.2.0=hdf63c60_1
• cffi==1.12.2=py37h2e261b9_1
• libtiff==4.0.10=h2733197_2
• kiwisolver==1.0.1=py37hf484d3e_0
• cudatoolkit==10.0.130=0
• pyqt==5.9.2=py37h05f1152_2
• pillow==5.4.1=py37h34e0f95_0
• icu==58.2=h9c2bf20_1
• libedit==3.1.20181209=hc058e9b_0
• xz==5.2.4=h14c3975_4
• libxcb==1.13=h1bed415_1
• libffi==3.2.1=hd88cf55_4
• mkl_random==1.0.2=py37hd81dba3_0
• python==3.7.3=h0371630_0
• pixman==0.38.0=h7b6447c_0
• mkl_fft==1.0.12=py37ha843d7b_0
• gmp==6.1.2=h6c8ec71_1
• libgfortran-ng==7.3.0=hdf63c60_0
• tornado==6.0.2=py37h7b6447c_0
• libuuid==1.0.3=h1bed415_2
• pcre==8.43=he6710b0_0
• py-boost==1.67.0=py37h04863e7_4
• yaml==0.1.7=had09818_2
• gst-plugins-base==1.14.0=hbbd80ab_1
• libsodium==1.0.16=h1bed415_0
• readline==7.0=h7b6447c_5
• pytorch==1.1.0=py3.7_cuda10.0.130_cudnn7.5.1_0
• freetype==2.9.1=h8a8886c_1
• sip==4.19.8=py37hf484d3e_0
• rdkit==2019.03.1.0=py37hc20afe1_1
• openssl==1.1.1c=h7b6447c_1
• numpy-base==1.16.4=py37hde5b4d6_0
• scipy==1.2.1=py37h7c811a0_0
• ncurses==6.1=he6710b0_1
• jpeg==9b=h024ee3a_2
• mistune==0.8.4=py37h7b6447c_0
• qt==5.9.7=h5867ecd_1
• dbus==1.13.6=h746ee38_0
• libpng==1.6.36=hbc83047_0
• expat==2.2.6=he6710b0_0
• sqlite==3.27.2=h7b6447c_0
• libboost==1.67.0=h46d08c1_4
• zlib==1.2.11=h7b6447c_3
• tk==8.6.8=hbc83047_0
• libstdcxx-ng==8.2.0=hdf63c60_1
• pandas==0.24.2=py37he6710b0_0
• libxml2==2.9.9=he19cac6_0
• bzip2==1.0.6=h14c3975_5
• matplotlib==3.0.3=py37h5429711_0
• cairo==1.14.12=h8948797_3
• gstreamer==1.14.0=hb453b48_1
• pyrsistent==0.14.11=py37h7b6447c_0
• pyyaml==5.1=py37h7b6447c_0
• markupsafe==1.1.1=py37h7b6447c_0
• cudnn==7.3.1=cuda10.0_0
• pyzmq==18.0.0=py37he6710b0_0
• ninja==1.9.0=py37hfd86e86_0
• zeromq==4.3.1=he6710b0_3
• scikit-learn==0.20.3=py37hd81dba3_0

and so, tried to install independently torchgraph:
$ pip install --user https://github.com/baldassarreFe/torchgraphs

Collecting https://github.com/baldassarreFe/torchgraphs
Downloading https://github.com/baldassarreFe/torchgraphs
- 121 kB 1.2 MB/s
ERROR: Cannot unpack file /private/var/folders/mj/fk1t3xm53gn_sd13cv2dq22c0000gp/T/pip-unpack-wt1i2ex1/torchgraphs (downloaded from /private/var/folders/mj/fk1t3xm53gn_sd13cv2dq22c0000gp/T/pip-req-build-xyu4u4km, content-type: text/html; charset=utf-8); cannot detect archive format
ERROR: Cannot determine archive format of /private/var/folders/mj/fk1t3xm53gn_sd13cv2dq22c0000gp/T/pip-req-build-xyu4u4km

Question on how Message Passing is performed on torchgraphs

I am currently trying to implement LRP on my own graph neural network, focused on discovering relevances at the node feature level (on my own implementation) where node features are roughly updated as:

H_next = (A.dot(H)).dot(W)

I see however, that the torchgraph implementation, and thus the LRP step in your research solely multiplies the node features with the weights.

Thus I would like to better understand how the node adjacencies are taken into consideration for the computations?

Thank you, and thank you for the great research and examples!