**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.

**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.

Mobile Neural Network

MNN: A Universal and Efficient Inference Engine

LightGCN

LightGCN: Simplifying and Powering Graph Convolution Network for Recommendation

**Mixed Attention Block** is an attention module used in the [ConvBERT](https://paperswithcode.com/method/convbert) architecture. It is a mixture of [self-attention](https://paperswithcode.com/method/scaled) and [span-based dynamic convolution](https://paperswithcode.com/method/span-based-dynamic-convolution) (highlighted in pink). They share the same Query but use different Key to generate the attention map and [convolution](https://paperswithcode.com/method/convolution) kernel respectively. The number of attention heads is reducing by directly projecting the input to a smaller embedding space to form a bottleneck structure for self-attention and span-based dynamic convolution. Dimensions of the input and output of some blocks are labeled on the left top corner to illustrate the overall framework, where $d$ is the embedding size of the input and $\gamma$ is the reduction ratio.

Source	LightGCN: Simplifying and Powering Graph Convolution Network for Recommendation
Year	2000
Data Source	CC BY-SA - https://paperswithcode.com