Stochastic Steady-state Embedding (SSE) is an algorithm that can learn many steady-state algorithms over graphs. Different from graph neural network family models, SSE is trained stochastically which only requires 1-hop information, but can capture fixed point relationships efficiently and effectively.

Description and Image from: [Learning Steady-States of Iterative Algorithms over Graphs](https://proceedings.mlr.press/v80/dai18a.html)

The **Lovasz-Softmax loss** is a loss function for multiclass semantic segmentation that incorporates the [softmax](https://paperswithcode.com/method/softmax) operation in the Lovasz extension. The Lovasz extension is a means by which we can achieve direct optimization of the mean intersection-over-union loss in neural networks.

Lovasz-Softmax

Learning Steady-States of Iterative Algorithms over Graphs

Recurrent Event Network (RE-NET) is an autoregressive architecture for predicting future interactions. The occurrence of a fact (event) is modeled as a probability distribution conditioned on temporal sequences of past knowledge graphs. RE-NET employs a recurrent event encoder to encode past facts and uses a neighborhood aggregator to model the connection of facts at the same timestamp. Future facts can then be inferred in a sequential manner based on the two modules.

Source	Learning Steady-States of Iterative Algorithms over Graphs
Year	2000
Data Source	CC BY-SA - https://paperswithcode.com