**Mobile Neural Network (MNN)** is a mobile inference engine tailored to mobile applications. The contributions of MNN include: (1) presenting a mechanism called pre-inference that manages to conduct runtime optimization; (2) delivering thorough kernel optimization on operators to achieve optimal computation performance; (3) introducing backend abstraction module which enables hybrid scheduling and keeps the engine lightweight.

Gated Graph Sequence Neural Networks (GGS-NNs) is a novel graph-based neural network model. GGS-NNs modifies Graph Neural Networks (Scarselli et al., 2009) to use gated recurrent units and modern optimization techniques and then extend to output sequences.

Source: [Li et al.](https://arxiv.org/pdf/1511.05493v4.pdf)

Image source: [Li et al.](https://arxiv.org/pdf/1511.05493v4.pdf)

GGS-NNs

Gated Graph Sequence Neural Networks

Mobile Neural Network

MNN: A Universal and Efficient Inference Engine

**LightGCN** is a type of [graph convolutional neural network](https://paperswithcode.com/method/gcn) (GCN), including only the most essential component in GCN (neighborhood aggregation) for collaborative filtering. Specifically, LightGCN learns user and item embeddings by linearly propagating them on the user-item interaction graph, and uses the weighted sum of the embeddings learned at all layers as the final embedding.

Source	MNN: A Universal and Efficient Inference Engine
Year	2000
Data Source	CC BY-SA - https://paperswithcode.com