301 research outputs found
Representation Learning on Graphs: A Reinforcement Learning Application
In this work, we study value function approximation in reinforcement learning
(RL) problems with high dimensional state or action spaces via a generalized
version of representation policy iteration (RPI). We consider the limitations
of proto-value functions (PVFs) at accurately approximating the value function
in low dimensions and we highlight the importance of features learning for an
improved low-dimensional value function approximation. Then, we adopt different
representation learning algorithm on graphs to learn the basis functions that
best represent the value function. We empirically show that node2vec, an
algorithm for scalable feature learning in networks, and the Variational Graph
Auto-Encoder constantly outperform the commonly used smooth proto-value
functions in low-dimensional feature space
Contrastive Learning for Non-Local Graphs with Multi-Resolution Structural Views
Learning node-level representations of heterophilic graphs is crucial for
various applications, including fraudster detection and protein function
prediction. In such graphs, nodes share structural similarity identified by the
equivalence of their connectivity which is implicitly encoded in the form of
higher-order hierarchical information in the graphs. The contrastive methods
are popular choices for learning the representation of nodes in a graph.
However, existing contrastive methods struggle to capture higher-order graph
structures. To address this limitation, we propose a novel multiview
contrastive learning approach that integrates diffusion filters on graphs. By
incorporating multiple graph views as augmentations, our method captures the
structural equivalence in heterophilic graphs, enabling the discovery of hidden
relationships and similarities not apparent in traditional node
representations. Our approach outperforms baselines on synthetic and real
structural datasets, surpassing the best baseline by on Cornell,
on Texas, and on Wisconsin. Additionally, it consistently
achieves superior performance on proximal tasks, demonstrating its
effectiveness in uncovering structural information and improving downstream
applications
Multiresolution Graph Transformers and Wavelet Positional Encoding for Learning Hierarchical Structures
Contemporary graph learning algorithms are not well-defined for large
molecules since they do not consider the hierarchical interactions among the
atoms, which are essential to determine the molecular properties of
macromolecules. In this work, we propose Multiresolution Graph Transformers
(MGT), the first graph transformer architecture that can learn to represent
large molecules at multiple scales. MGT can learn to produce representations
for the atoms and group them into meaningful functional groups or repeating
units. We also introduce Wavelet Positional Encoding (WavePE), a new positional
encoding method that can guarantee localization in both spectral and spatial
domains. Our proposed model achieves competitive results on two macromolecule
datasets consisting of polymers and peptides, and one drug-like molecule
dataset. Importantly, our model outperforms other state-of-the-art methods and
achieves chemical accuracy in estimating molecular properties (e.g., GAP, HOMO
and LUMO) calculated by Density Functional Theory (DFT) in the polymers
dataset. Furthermore, the visualizations, including clustering results on
macromolecules and low-dimensional spaces of their representations, demonstrate
the capability of our methodology in learning to represent long-range and
hierarchical structures. Our PyTorch implementation is publicly available at
https://github.com/HySonLab/Multires-Graph-Transforme
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