A survey of graph neural networks for social recommender systems

K Sharma, YC Lee, S Nambi, A Salian, S Shah… - ACM Computing …, 2024 - dl.acm.org
Social recommender systems (SocialRS) simultaneously leverage the user-to-item
interactions as well as the user-to-user social relations for the task of generating item …

G-mixup: Graph data augmentation for graph classification

X Han, Z Jiang, N Liu, X Hu - International Conference on …, 2022 - proceedings.mlr.press
This work develops mixup for graph data. Mixup has shown superiority in improving the
generalization and robustness of neural networks by interpolating features and labels …

Towards data augmentation in graph neural network: An overview and evaluation

M Adjeisah, X Zhu, H Xu, TA Ayall - Computer Science Review, 2023 - Elsevier
Abstract Many studies on Graph Data Augmentation (GDA) approaches have emerged. The
techniques have rapidly improved performance for various graph neural network (GNN) …

Unleashing the power of graph data augmentation on covariate distribution shift

Y Sui, Q Wu, J Wu, Q Cui, L Li, J Zhou… - Advances in Neural …, 2024 - proceedings.neurips.cc
The issue of distribution shifts is emerging as a critical concern in graph representation
learning. From the perspective of invariant learning and stable learning, a recently well …

Condensing graphs via one-step gradient matching

W Jin, X Tang, H Jiang, Z Li, D Zhang, J Tang… - Proceedings of the 28th …, 2022 - dl.acm.org
As training deep learning models on large dataset takes a lot of time and resources, it is
desired to construct a small synthetic dataset with which we can train deep learning models …

Graph rationalization with environment-based augmentations

G Liu, T Zhao, J Xu, T Luo, M Jiang - Proceedings of the 28th ACM …, 2022 - dl.acm.org
Rationale is defined as a subset of input features that best explains or supports the
prediction by machine learning models. Rationale identification has improved the …

Out-of-distribution generalization on graphs: A survey

H Li, X Wang, Z Zhang, W Zhu - arXiv preprint arXiv:2202.07987, 2022 - arxiv.org
Graph machine learning has been extensively studied in both academia and industry.
Although booming with a vast number of emerging methods and techniques, most of the …

Graph anomaly detection via multi-scale contrastive learning networks with augmented view

J Duan, S Wang, P Zhang, E Zhu, J Hu, H Jin… - Proceedings of the …, 2023 - ojs.aaai.org
Graph anomaly detection (GAD) is a vital task in graph-based machine learning and has
been widely applied in many real-world applications. The primary goal of GAD is to capture …

Linkless link prediction via relational distillation

Z Guo, W Shiao, S Zhang, Y Liu… - International …, 2023 - proceedings.mlr.press
Abstract Graph Neural Networks (GNNs) have shown exceptional performance in the task of
link prediction. Despite their effectiveness, the high latency brought by non-trivial …

GRAPHPATCHER: mitigating degree bias for graph neural networks via test-time augmentation

M Ju, T Zhao, W Yu, N Shah… - Advances in Neural …, 2024 - proceedings.neurips.cc
Recent studies have shown that graph neural networks (GNNs) exhibit strong biases
towards the node degree: they usually perform satisfactorily on high-degree nodes with rich …