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Descent Steps of a Relation-Aware Energy Produce Heterogeneous Graph Neural Networks
Hongjoon Ahn · Yongyi Yang · Quan Gan · Taesup Moon · David P Wipf

Wed Nov 30 09:00 AM -- 11:00 AM (PST) @ Hall J #236

Heterogeneous graph neural networks (GNNs) achieve strong performance on node classification tasks in a semi-supervised learning setting. However, as in the simpler homogeneous GNN case, message-passing-based heterogeneous GNNs may struggle to balance between resisting the oversmoothing that may occur in deep models, and capturing long-range dependencies of graph structured data. Moreover, the complexity of this trade-off is compounded in the heterogeneous graph case due to the disparate heterophily relationships between nodes of different types. To address these issues, we propose a novel heterogeneous GNN architecture in which layers are derived from optimization steps that descend a novel relation-aware energy function. The corresponding minimizer is fully differentiable with respect to the energy function parameters, such that bilevel optimization can be applied to effectively learn a functional form whose minimum provides optimal node representations for subsequent classification tasks. In particular, this methodology allows us to model diverse heterophily relationships between different node types while avoiding oversmoothing effects. Experimental results on 8 heterogeneous graph benchmarks demonstrates that our proposed method can achieve competitive node classification accuracy.

Author Information

Hongjoon Ahn (Seoul National University)
Yongyi Yang (University of Michigan)
Quan Gan (New York University)
Taesup Moon (Seoul National University (SNU))

Taesup Moon is currently an associate professor at Seoul National University (SNU), Korea. Prior to joining SNU in 2021, he was an associate professor at Sungkyunkwan University (SKKU) from 2017 to 2021, an assistant professor at DGIST from 2015 to 2017, a research staff member at Samsung Advanced Institute of Technology (SAIT) from 2013 to 2015, a postdoctoral researcher at UC Berkeley, Statistics from 2012 to 2013, and a research scientist at Yahoo! Labs from 2008 to 2012. He got his Ph.D. and MS degrees in Electrical Engineering from Stanford University, CA USA in 2008 and 2004, respectively, and his BS degree in Electrical Engineering from Seoul National University, Korea in 2002. His research interests are in deep learning, statistical machine learning, data science, signal processing, and information theory.

David P Wipf (AWS)

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