Temporal graphs offer more accurate modeling of many real-world scenarios
than static graphs. However, neighbor aggregation, a critical building block of
graph networks, for temporal graphs, is currently straightforwardly extended
from that of static graphs. It can be computationally expensive when involving
all historical neighbors during such aggregation. In practice, typically only a
subset of the most recent neighbors are involved. However, such subsampling
leads to incomplete and biased neighbor information. To address this
limitation, we propose a novel framework for temporal neighbor aggregation that
uses the recurrent neural network with node-wise hidden states to integrate
information from all historical neighbors for each node to acquire the complete
neighbor information. We demonstrate the superior theoretical expressiveness of
the proposed framework as well as its state-of-the-art performance in
real-world applications. Notably, it achieves a significant +9.6% improvement
on averaged precision in a real-world Ecommerce dataset over existing methods
on 2-layer models.

用递归神经网络对时态图的邻居进行汇聚，以获取来自每个节点的完整邻居信息，从而提供具有卓越的理论表达能力和在真实应用中的最先进性能，其中在一个真实世界的电子商务数据集上，相比于现有模型的 2 层模型，平均精确度显著提高了 9.6%。

循环时序修正图网络

Recurrent Temporal Revision Graph Networks

Graph collaborative filtering (GCF) is a popular technique for capturing
high-order collaborative signals in recommendation systems. However, GCF's
bipartite adjacency matrix, which defines the neighbors being aggregated based
on user-item interactions, can be noisy for users/items with abundant
interactions and insufficient for users/items with scarce interactions.
Additionally, the adjacency matrix ignores user-user and item-item
correlations, which can limit the scope of beneficial neighbors being
aggregated.
In this work, we propose a new graph adjacency matrix that incorporates
user-user and item-item correlations, as well as a properly designed user-item
interaction matrix that balances the number of interactions across all users.
To achieve this, we pre-train a graph-based recommendation method to obtain
users/items embeddings, and then enhance the user-item interaction matrix via
top-K sampling. We also augment the symmetric user-user and item-item
correlation components to the adjacency matrix. Our experiments demonstrate
that the enhanced user-item interaction matrix with improved neighbors and
lower density leads to significant benefits in graph-based recommendation.
Moreover, we show that the inclusion of user-user and item-item correlations
can improve recommendations for users with both abundant and insufficient
interactions. The code is in https://github.com/zfan20/GraphDA.

本文提出了一种新的图矩阵和用户 - 项目交互矩阵的设计方法，将用户 - 用户、项目 - 项目相关性以及用户 - 项目交互数量平衡考虑在内，提高了高阶协同信号的捕获质量，推荐效果显著提升。

图协同信号去噪与增强用于推荐

Graph Collaborative Signals Denoising and Augmentation for  Recommendation

Graph Neural Networks (GNNs) are recently proposed neural network structures
for the processing of graph-structured data. Due to their employed neighbor
aggregation strategy, existing GNNs focus on capturing node-level information
and neglect high-level information. Existing GNNs therefore suffer from
representational limitations caused by the Local Permutation Invariance (LPI)
problem. To overcome these limitations and enrich the features captured by
GNNs, we propose a novel GNN framework, referred to as the Two-level GNN
(TL-GNN). This merges subgraph-level information with node-level information.
Moreover, we provide a mathematical analysis of the LPI problem which
demonstrates that subgraph-level information is beneficial to overcoming the
problems associated with LPI. A subgraph counting method based on the dynamic
programming algorithm is also proposed, and this has time complexity is O(n^3),
n is the number of nodes of a graph. Experiments show that TL-GNN outperforms
existing GNNs and achieves state-of-the-art performance.

我们提出了一种被称为 Two-level GNN 的新型 GNN 框架，通过将子图级别信息与节点级别信息合并来克服现有 GNN 的局部置换不变性问题，并提出了一个基于动态规划算法的子图计数方法，实验证明 TL-GNN 优于现有 GNN，并实现了最先进的性能。