8 research outputs found
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
Node Copying: A Random Graph Model for Effective Graph Sampling
There has been an increased interest in applying machine learning techniques
on relational structured-data based on an observed graph. Often, this graph is
not fully representative of the true relationship amongst nodes. In these
settings, building a generative model conditioned on the observed graph allows
to take the graph uncertainty into account. Various existing techniques either
rely on restrictive assumptions, fail to preserve topological properties within
the samples or are prohibitively expensive for larger graphs. In this work, we
introduce the node copying model for constructing a distribution over graphs.
Sampling of a random graph is carried out by replacing each node's neighbors by
those of a randomly sampled similar node. The sampled graphs preserve key
characteristics of the graph structure without explicitly targeting them.
Additionally, sampling from this model is extremely simple and scales linearly
with the nodes. We show the usefulness of the copying model in three tasks.
First, in node classification, a Bayesian formulation based on node copying
achieves higher accuracy in sparse data settings. Second, we employ our
proposed model to mitigate the effect of adversarial attacks on the graph
topology. Last, incorporation of the model in a recommendation system setting
improves recall over state-of-the-art methods