62,992 research outputs found
Bayesian Semi-supervised Learning with Graph Gaussian Processes
We propose a data-efficient Gaussian process-based Bayesian approach to the
semi-supervised learning problem on graphs. The proposed model shows extremely
competitive performance when compared to the state-of-the-art graph neural
networks on semi-supervised learning benchmark experiments, and outperforms the
neural networks in active learning experiments where labels are scarce.
Furthermore, the model does not require a validation data set for early
stopping to control over-fitting. Our model can be viewed as an instance of
empirical distribution regression weighted locally by network connectivity. We
further motivate the intuitive construction of the model with a Bayesian linear
model interpretation where the node features are filtered by an operator
related to the graph Laplacian. The method can be easily implemented by
adapting off-the-shelf scalable variational inference algorithms for Gaussian
processes.Comment: To appear in NIPS 2018 Fixed an error in Figure 2. The previous arxiv
version contains two identical sub-figure
Topics in Graph Construction for Semi-Supervised Learning
Graph-based Semi-Supervised Learning (SSL) methods have had empirical success in a variety of domains, ranging from natural language processing to bioinformatics. Such methods consist of two phases. In the first phase, a graph is constructed from the available data; in the second phase labels are inferred for unlabeled nodes in the constructed graph. While many algorithms have been developed for label inference, thus far little attention has been paid to the crucial graph construction phase and only recently has the importance of the graph construction for the resulting success in label inference been recognized. In this report, we shall review some of the recently proposed graph construction methods for graph-based SSL. We shall also present suggestions for future research in this area
Semi-Supervised Node Classification on Graphs: Markov Random Fields vs. Graph Neural Networks
Semi-supervised node classification on graph-structured data has many
applications such as fraud detection, fake account and review detection, user's
private attribute inference in social networks, and community detection.
Various methods such as pairwise Markov Random Fields (pMRF) and graph neural
networks were developed for semi-supervised node classification. pMRF is more
efficient than graph neural networks. However, existing pMRF-based methods are
less accurate than graph neural networks, due to a key limitation that they
assume a heuristics-based constant edge potential for all edges. In this work,
we aim to address the key limitation of existing pMRF-based methods. In
particular, we propose to learn edge potentials for pMRF. Our evaluation
results on various types of graph datasets show that our optimized pMRF-based
method consistently outperforms existing graph neural networks in terms of both
accuracy and efficiency. Our results highlight that previous work may have
underestimated the power of pMRF for semi-supervised node classification.Comment: Accepted by AAAI 202
- …