This paper analyses LightGCN in the context of graph recommendation
algorithms. Despite the initial design of Graph Convolutional Networks for
graph classification, the non-linear operations are not always essential.
LightGCN enables linear propagation of embeddings, enhancing performance. We
reproduce the original findings, assess LightGCN's robustness on diverse
datasets and metrics, and explore Graph Diffusion as an augmentation of signal
propagation in LightGCN.

本论文在图推荐算法中分析了 LightGCN。尽管图卷积网络最初的设计是用于图分类，但非线性操作并不总是必要的。LightGCN 能够实现嵌入的线性传播，从而提升性能。我们重现了原始发现，评估了 LightGCN 在不同数据集和指标上的鲁棒性，并探索了作为 LightGCN 信号传播增强的图扩散方法。

LightGCN: 评估和增强

LightGCN: Evaluated and Enhanced

Graph neural networks (GNNs) have gained wide popularity in recommender
systems due to their capability to capture higher-order structure information
among the nodes of users and items. However, these methods need to collect
personal interaction data between a user and the corresponding items and then
model them in a central server, which would break the privacy laws such as
GDPR. So far, no existing work can construct a global graph without leaking
each user's private interaction data (i.e., his or her subgraph). In this
paper, we are the first to design a novel lossless federated recommendation
framework based on GNN, which achieves full-graph training with complete
high-order structure information, enabling the training process to be
equivalent to the corresponding un-federated counterpart. In addition, we use
LightGCN to instantiate an example of our framework and show its equivalence.

本文设计了一种基于 GNN 的全局无损联合推荐框架，可以实现完整的高阶结构信息的全图训练，避免了个人互动数据泄露，同时使用 LightGCN 实现了框架的等效性。

基于无损 GNN 的联邦推荐框架 GNN4FR

GNN4FR: A Lossless GNN-based Federated Recommendation Framework

Graph Convolution Network (GCN) has become new state-of-the-art for
collaborative filtering. Nevertheless, the reasons of its effectiveness for
recommendation are not well understood. Existing work that adapts GCN to
recommendation lacks thorough ablation analyses on GCN, which is originally
designed for graph classification tasks and equipped with many neural network
operations. However, we empirically find that the two most common designs in
GCNs -- feature transformation and nonlinear activation -- contribute little to
the performance of collaborative filtering. Even worse, including them adds to
the difficulty of training and degrades recommendation performance.
In this work, we aim to simplify the design of GCN to make it more concise
and appropriate for recommendation. We propose a new model named LightGCN,
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. Such simple, linear, and neat model is much easier to implement and
train, exhibiting substantial improvements (about 16.0\% relative improvement
on average) over Neural Graph Collaborative Filtering (NGCF) -- a
state-of-the-art GCN-based recommender model -- under exactly the same
experimental setting. Further analyses are provided towards the rationality of
the simple LightGCN from both analytical and empirical perspectives.

研究采用邻域聚合操作来简化 GCN 模型设计，提出了名为 LightGCN 的新模型，用于协同过滤任务上取得了比基于 GCN 的 Neural Graph Collaborative Filtering 模型高出 16.0% 的性能改善，分析表明 LightGCN 相比于其他 GCN-based 推荐模型更加合理且易于实现和训练。