(Rex) Devon Hjelm

Deep Graph Infomax

William Fedus

William L. Hamilton

Pietro Lio

2018-09-27

ArXiv (prépublication)

Deep Graph Infomax

William Fedus

William L. Hamilton

Pietro Lio

We present Deep Graph Infomax (DGI), a general approach for learning node representations within graph-structured data in an unsupervised ma… (voir plus)nner. DGI relies on maximizing mutual information between patch representations and corresponding high-level summaries of graphs---both derived using established graph convolutional network architectures. The learnt patch representations summarize subgraphs centered around nodes of interest, and can thus be reused for downstream node-wise learning tasks. In contrast to most prior approaches to unsupervised learning with GCNs, DGI does not rely on random walk objectives, and is readily applicable to both transductive and inductive learning setups. We demonstrate competitive performance on a variety of node classification benchmarks, which at times even exceeds the performance of supervised learning.

2018-09-27

ArXiv (prépublication)

Deep Graph Infomax

William Fedus

William L. Hamilton

Pietro Lio

2018-09-27

ArXiv (prépublication)

Deep Graph Infomax

William Fedus

William L. Hamilton

Pietro Lio

2018-09-27

ArXiv (prépublication)

Deep Graph Infomax

William Fedus

William L. Hamilton

Pietro Lio

2018-09-27

ArXiv (prépublication)

Deep Graph Infomax

William Fedus

William L. Hamilton

Pietro Lio

We present Deep Graph Infomax (DGI), a general approach for learning node representations within graph-structured data in an unsupervised ma… (voir plus)nner. DGI relies on maximizing mutual information between patch representations and corresponding high-level summaries of graphs---both derived using established graph convolutional network architectures. The learnt patch representations summarize subgraphs centered around nodes of interest, and can thus be reused for downstream node-wise learning tasks. In contrast to most prior approaches to unsupervised learning with GCNs, DGI does not rely on random walk objectives, and is readily applicable to both transductive and inductive learning setups. We demonstrate competitive performance on a variety of node classification benchmarks, which at times even exceeds the performance of supervised learning.

2018-09-27

ArXiv (prépublication)

Deep Graph Infomax

William Fedus

William L. Hamilton

Pietro Lio

2018-09-27

ArXiv (prépublication)

Deep Graph Infomax

William Fedus

William L. Hamilton

Pietro Lio

2018-09-27

ArXiv (prépublication)

Deep Graph Infomax

William Fedus

William L. Hamilton

Pietro Lio

We present Deep Graph Infomax (DGI), a general approach for learning node representations within graph-structured data in an unsupervised ma… (voir plus)nner. DGI relies on maximizing mutual information between patch representations and corresponding high-level summaries of graphs---both derived using established graph convolutional network architectures. The learnt patch representations summarize subgraphs centered around nodes of interest, and can thus be reused for downstream node-wise learning tasks. In contrast to most prior approaches to unsupervised learning with GCNs, DGI does not rely on random walk objectives, and is readily applicable to both transductive and inductive learning setups. We demonstrate competitive performance on a variety of node classification benchmarks, which at times even exceeds the performance of supervised learning.

2018-09-27

ArXiv (prépublication)

Mutual Information Neural Estimation

Ishmael Belghazi

Sai Rajeswar

We argue that the estimation of mutual information between high dimensional continuous random variables can be achieved by gradient descent … (voir plus)over neural networks. We present a Mutual Information Neural Estimator (MINE) that is linearly scalable in dimensionality as well as in sample size, trainable through back-prop, and strongly consistent. We present a handful of applications on which MINE can be used to minimize or maximize mutual information. We apply MINE to improve adversarially trained generative models. We also use MINE to implement Information Bottleneck, applying it to supervised classification; our results demonstrate substantial improvement in flexibility and performance in these settings.

2018-07-03

Proceedings of the 35th International Conference on Machine Learning (publié)

proceedings.mlr.press

MINE: Mutual Information Neural Estimation

Ishmael Belghazi

Sai Rajeswar

Aristide Baratin