Publications

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

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

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

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)