A tensorflow implementation of GCN-LPA

Related tags

Deep Learning label-propagation graph-convolutional-networks
Overview

GCN-LPA

This repository is the implementation of GCN-LPA (arXiv):

Unifying Graph Convolutional Neural Networks and Label Propagation
Hongwei Wang, Jure Leskovec
arXiv Preprint, 2020

GCN-LPA is an end-to-end model that unifies Graph Convolutional Neural Networks (GCN) and Label Propagation Algorithm (LPA) for adaptive semi-supervised node classification.

Files in the folder

  • data/
    • citeseer/
    • cora/
    • pubmed/
    • ms_academic_cs.npz (Coauthor-CS)
    • ms_academic_phy.npz (Coauthor-Phy)
  • src/: implementation of GCN-LPA.

Running the code

$ python main.py

Note: The default dataset is Citeseer. Hyper-parameter settings for other datasets are provided in main.py.

Required packages

The code has been tested running under Python 3.6.5, with the following packages installed (along with their dependencies):

  • tensorflow == 1.12.0
  • networkx == 2.1
  • numpy == 1.14.3
  • scipy == 1.1.0
  • sklearn == 0.19.1
  • matplotlib == 2.2.2
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Comments
  • AttributeError: module 'tensorflow' has no attribute 'set_random_seed'

    AttributeError: module 'tensorflow' has no attribute 'set_random_seed'

    Awesome work!

    I came across this error running your code:

    Traceback (most recent call last):
  File "main.py", line 10, in <module>
    tf.set_random_seed(seed)
AttributeError: module 'tensorflow' has no attribute 'set_random_seed'

    set_random_seed() is deprecated and it has been moved to tf.random.set_seed() https://www.tensorflow.org/versions/r2.0/api_docs/python/tf/random/set_seed

    opened by jayroxis 1
  • Reset Labels for Labeled Nodes in LPA

    Reset Labels for Labeled Nodes in LPA

    Hi,

    In "model.py" when building lpa as following code, the labeled nodes seem not to be reset in the loop. I am wondering if this is correct. thank you! def _build_lpa(self): label_mask = tf.expand_dims(self.label_mask, -1) input_labels = label_mask * self.labels label_list = [input_labels]

        for _ in range(self.args.lpa_iter):
        lp_layer = LPALayer(adj=self.normalized_adj)
        hidden = lp_layer(label_list[-1])
        label_list.append(hidden)
    self.predicted_label = label_list[-1]
    opened by unbreakablegao 3
  • please share ppt

    please share ppt

    https://www.techbeat.net/articles/MTU5MTE5NDA3OTA5Ny03MzgtNjc2MTQ=

    This one is awesome, can you create a repo and share your ppt, or send me by email? Thank you ~

    opened by max-yue 0
Owner
Hongwei Wang
Graph neural networks, knowledge graphs, recommender systems
Hongwei Wang
GitHub
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