GAM
Manually cloned repository of Graph Agreement Model.
GAM: Graph Agreement Models for Semi-Supervised Learning
This code repository contains an implementation of Graph Agreement Models [1].
Neural structured learning methods such as Neural Graph Machines [2], Graph Convolutional Networks [3] and their variants have successfully combined the expressiveness of neural networks with graph structures to improve on learning tasks. Graph Agreement Models (GAM) is a technique that can be applied to these methods to handle the noisy nature of real-world graphs. Traditional graph-based algorithms, such as label propagation, were designed with the underlying assumption that the label of a node can be imputed from that of the neighboring nodes and edge weights. However, most real-world graphs are either noisy or have edges that do not correspond to label agreement uniformly across the graph. Graph Agreement Models introduce an auxiliary model that predicts the probability of two nodes sharing the same label as a learned function of their features. This agreement model is then used when training a node classification model by encouraging agreement only for those pairs of nodes that it deems likely to have the same label, thus guiding its parameters to a better local optima. The classification and agreement models are trained jointly in a co-training fashion.
The code is organized into the following folders:
- data: Classes and methods for accessing semi-supervised learning datasets.
- models: Classes and methods for classification models and graph agreement models.
- trainer: Classes and methods for training the classification models, and agreement models individually as well as in a co-training fashion.
- experiments: Python run script for training Graph Agreement Models on CIFAR10 and other datasets.
The implementations of Graph Agreement Models (GAMs) are provided in the gam
folder on a strict "as is" basis, without warranties or conditions of any kind.
Also, these implementations may not be compatible with certain TensorFlow
versions (such as 2.0 or above) or Python versions.
How to run
To run GAM on a graph-based dataset (e.g., Cora, Citeseer, Pubmed), from this
folder run: bash python3.7 -m gam.experiments.run_train_gam_graph --data_path=<path_to_data>
To run GAM on datasets without a graph (e.g., CIFAR10), from this folder run:
bash python3.7 -m gam.experiments.run_train_gam
For running on different datasets and configuration, please check the command line flags in each of the run scripts.