Unsupervised Joint -node Graph Representations with Compositional Energy-Based Models
arXiv:2010.04259
Abstract
Existing Graph Neural Network (GNN) methods that learn inductive unsupervised graph representations focus on learning node and edge representations by predicting observed edges in the graph. Although such approaches have shown advances in downstream node classification tasks, they are ineffective in jointly representing larger -node sets, . We propose MHM-GNN, an inductive unsupervised graph representation approach that combines joint -node representations with energy-based models (hypergraph Markov networks) and GNNs. To address the intractability of the loss that arises from this combination, we endow our optimization with a loss upper bound using a finite-sample unbiased Markov Chain Monte Carlo estimator. Our experiments show that the unsupervised MHM-GNN representations of MHM-GNN produce better unsupervised representations than existing approaches from the literature.
arXiv commentaccepted at NeurIPS 2020
References in corpus (15)
- PyTorch: An Imperative Style, High-Performance Deep Learning Library
- Inductive Representation Learning on Large Graphs
- Fast Graph Representation Learning with PyTorch Geometric
- Variational Graph Auto-Encoders
- Simplicial Closure and higher-order link prediction
- graph2vec: Learning Distributed Representations of Graphs
- NetLSD: Hearing the Shape of a Graph
- Invariant and Equivariant Graph Networks
- Learning for Safety-Critical Control with Control Barrier Functions
- Weisfeiler and Leman go sparse: Towards scalable higher-order graph embeddings
- Hypergraph Convolution and Hypergraph Attention
- On the Equivalence between Positional Node Embeddings and Structural Graph Representations
- Residual Energy-Based Models for Text
- Recovering Metabolic Networks using A Novel Hyperlink Prediction Method
- Neural-Swarm: Decentralized Close-Proximity Multirotor Control Using Learned Interactions