Paper Info
Title
Graph Agreement Models for Semi-Supervised Learning
Abstract
Graph-based algorithms are among the most successful paradigms for solving semi-supervised learning tasks. Recent work on graph convolutional networks and neural graph learning methods has successfully combined the expressiveness of neural networks with graph structures. We propose a technique that, when applied to these methods, achieves state-of-the-art results on semi-supervised learning datasets. 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. However, real-world graphs are either noisy or have edges that do not correspond to label agreement. To address this, we propose Graph Agreement Models (GAM), which introduces an auxiliary model that predicts the probability of two nodes sharing the same label as a learned function of their features. The agreement model is used when training a node classification model by encouraging agreement only for the pairs of nodes it deems likely to have the same label, thus guiding its parameters to better local optima. The classification and agreement models are trained jointly in a co-training fashion. Moreover, GAM can also be applied to any semi-supervised classification problem, by inducing a graph whenever one is not provided. We demonstrate that our method achieves a relative improvement of up to 72% for various node classification models, and obtains state-of-the-art results on multiple established datasets.
Year
Venue
Keywords
2019
NeurIPS
neural networks,supervised learning,semi-supervised learning
Field
DocType
Citations 
Graph,Semi-supervised learning,Computer science,Artificial intelligence,Machine learning
Conference
0
PageRank 
References 
Authors
0.34
0
6
Name
Order
Citations
PageRank
Otilia Stretcu142.49
Krishnamurthy Viswanathan216513.29
Dana Movshovitz-Attias31155.62
Emmanouil Platanios400.34
Sujith Ravi553333.06
Andrew Tomkins693881401.23