A Survey on Self-Supervised Pre-Training of Graph Foundation Models: A Knowledge-Based Perspective

Graph self-supervised learning is now a go-to method for pre-training graph foundation models, including graph neural networks, graph transformers, and more recent large language model (LLM)-based graph models. There is a wide variety of knowledge patterns embedded in the structure and properties of graphs which may be used for pre-training, but we lack a systematic overview of self-supervised pre-training tasks from the perspective of graph knowledge. In this paper, we comprehensively survey and analyze the pre-training tasks of graph foundation models from a knowledge-based perspective, consisting of microscopic (nodes, links, etc) and macroscopic knowledge (clusters, global structure, etc). It covers a total of 9 knowledge categories and 25 pre-training tasks, as well as various downstream task adaptation strategies. Furthermore, an extensive list of the related papers with detailed metadata is provided at https://github.com/Newiz430/Pretext.

Flatten Long-Range Loss Landscapes for Cross-Domain Few-Shot Learning

Cross-domain few-shot learning (CDFSL) aims to acquire knowledge from limited training data in the target domain by leveraging prior knowledge transferred from source domains with abundant training samples. CDFSL faces challenges in transferring knowledge across dissimilar domains and fine-tuning models with limited training data. To address these challenges, we initially extend the analysis of loss landscapes from the parameter space to the representation space, which allows us to simultaneously interpret the transferring and fine-tuning difficulties of CDFSL models. We observe that sharp minima in the loss landscapes of the representation space result in representations that are hard to transfer and fine-tune. Moreover, existing flatness-based methods have limited generalization ability due to their short-range flatness. To enhance the transferability and facilitate fine-tuning, we introduce a simple yet effective approach to achieve long-range flattening of the minima in the loss landscape. This approach considers representations that are differently normalized as minima in the loss landscape and flattens the high-loss region in the middle by randomly sampling interpolated representations. We implement this method as a new normalization layer that replaces the original one in both CNNs and ViTs. This layer is simple and lightweight, introducing only a minimal number of additional parameters. Experimental results on 8 datasets demonstrate that our approach outperforms state-of-the-art methods in terms of average accuracy. Moreover, our method achieves performance improvements of up to 9\% compared to the current best approaches on individual datasets. Our code will be released.

Masked Graph Autoencoder with Non-discrete Bandwidths

Masked graph autoencoders have emerged as a powerful graph self-supervised learning method that has yet to be fully explored. In this paper, we unveil that the existing discrete edge masking and binary link reconstruction strategies are insufficient to learn topologically informative representations, from the perspective of message propagation on graph neural networks. These limitations include blocking message flows, vulnerability to over-smoothness, and suboptimal neighborhood discriminability. Inspired by these understandings, we explore non-discrete edge masks, which are sampled from a continuous and dispersive probability distribution instead of the discrete Bernoulli distribution. These masks restrict the amount of output messages for each edge, referred to as "bandwidths". We propose a novel, informative, and effective topological masked graph autoencoder using bandwidth masking and a layer-wise bandwidth prediction objective. We demonstrate its powerful graph topological learning ability both theoretically and empirically. Our proposed framework outperforms representative baselines in both self-supervised link prediction (improving the discrete edge reconstructors by at most 20%) and node classification on numerous datasets, solely with a structure-learning pretext. Our implementation is available at https://github.com/Newiz430/Bandana.

CSGCL: Community-Strength-Enhanced Graph Contrastive Learning

Graph Contrastive Learning (GCL) is an effective way to learn generalized graph representations in a self-supervised manner, and has grown rapidly in recent years. However, the underlying community semantics has not been well explored by most previous GCL methods. Research that attempts to leverage communities in GCL regards them as having the same influence on the graph, leading to extra representation errors. To tackle this issue, we define ''community strength'' to measure the difference of influence among communities. Under this premise, we propose a Community-Strength-enhanced Graph Contrastive Learning (CSGCL) framework to preserve community strength throughout the learning process. Firstly, we present two novel graph augmentation methods, Communal Attribute Voting (CAV) and Communal Edge Dropping (CED), where the perturbations of node attributes and edges are guided by community strength. Secondly, we propose a dynamic ''Team-up'' contrastive learning scheme, where community strength is used to progressively fine-tune the contrastive objective. We report extensive experiment results on three downstream tasks: node classification, node clustering, and link prediction. CSGCL achieves state-of-the-art performance compared with other GCL methods, validating that community strength brings effectiveness and generality to graph representations. Our code is available at https://github.com/HanChen-HUST/CSGCL.

Margin-Based Few-Shot Class-Incremental Learning with Class-Level Overfitting Mitigation

Few-shot class-incremental learning (FSCIL) is designed to incrementally recognize novel classes with only few training samples after the (pre-)training on base classes with sufficient samples, which focuses on both base-class performance and novel-class generalization. A well known modification to the base-class training is to apply a margin to the base-class classification. However, a dilemma exists that we can hardly achieve both good base-class performance and novel-class generalization simultaneously by applying the margin during the base-class training, which is still under explored. In this paper, we study the cause of such dilemma for FSCIL. We first interpret this dilemma as a class-level overfitting (CO) problem from the aspect of pattern learning, and then find its cause lies in the easily-satisfied constraint of learning margin-based patterns. Based on the analysis, we propose a novel margin-based FSCIL method to mitigate the CO problem by providing the pattern learning process with extra constraint from the margin-based patterns themselves. Extensive experiments on CIFAR100, Caltech-USCD Birds-200-2011 (CUB200), and miniImageNet demonstrate that the proposed method effectively mitigates the CO problem and achieves state-of-the-art performance.

Saliency Constrained Arbitrary Image Style Transfer using SIFT and DCNN

This paper develops a new image synthesis approach to transfer an example image (style image) to other images (content images) by using Deep Convolutional Neural Networks (DCNN) model. When common neural style transfer methods are used, the textures and colors in the style image are usually transferred imperfectly to the content image, or some visible errors are generated. This paper proposes a novel saliency constrained method to reduce or avoid such effects. It first evaluates some existing saliency detection methods to select the most suitable one for use in our method. The selected saliency detection method is used to detect the object in the style image, corresponding to the object of the content image with the same saliency. In addition, aim to solve the problem that the size or resolution is different in the style image and content, the scale-invariant feature transform is used to generate a series of style images and content images which can be used to generate more feature maps for patches matching. It then proposes a new loss function combining the saliency loss, style loss and content loss, adding gradient of saliency constraint into style transfer in iterations. Finally the source images and saliency detection results are utilized as multichannel input to an improved deep CNN framework for style transfer. The experiments show that the saliency maps of source images can help find the correct matching and avoid artifacts. Experimental results on different kind of images demonstrate that our method outperforms nine representative methods from recent publications and has good robustness.

Gradient Scheduling with Global Momentum for Non-IID Data Distributed Asynchronous Training

Distributed asynchronous offline training has received widespread attention in recent years because of its high performance on large-scale data and complex models. As data are processed from cloud-centric positions to edge locations, a big challenge for distributed systems is how to handle native and natural non-independent and identically distributed (non-IID) data for training. Previous asynchronous training methods do not have a satisfying performance on non-IID data because it would result in that the training process fluctuates greatly which leads to an abnormal convergence. We propose a gradient scheduling algorithm with global momentum (GSGM) for non-IID data distributed asynchronous training. Our key idea is to schedule the gradients contributed by computing nodes based on a white list so that each training node's update frequency remains even. Furthermore, our new momentum method can solve the biased gradient problem. GSGM can make model converge effectively, and maintain high availability eventually. Experimental results show that for non-IID data training under the same experimental conditions, GSGM on popular optimization algorithms can achieve an 20% increase in training stability with a slight improvement in accuracy on Fashion-Mnist and CIFAR-10 datasets. Meanwhile, when expanding distributed scale on CIFAR-100 dataset that results in sparse data distribution, GSGM can perform an 37% improvement on training stability. Moreover, only GSGM can converge well when the number of computing nodes is 30, compared to the state-of-the-art distributed asynchronous algorithms.