Deep Graph Infomax (DGI), a general approach for learning node representations within graph-structured data in an unsupervised manner. 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.

Description and image from: [DEEP GRAPH INFOMAX](https://arxiv.org/pdf/1809.10341.pdf)

Characterizable Invertible $3\times3$  Convolution

CInC Flow

CInC Flow: Characterizable Invertible 3x3 Convolution

Deep Graph Infomax

**CTRL** is conditional [transformer](https://paperswithcode.com/method/transformer) language model, trained
to condition on control codes that govern style, content, and task-specific behavior. Control codes were derived from structure that naturally co-occurs with raw
text, preserving the advantages of unsupervised learning while providing more
explicit control over text generation. These codes also allow CTRL to predict
which parts of the training data are most likely given a sequence

Source	Deep Graph Infomax
Year	2000
Data Source	CC BY-SA - https://paperswithcode.com