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

• URL: http://arxiv.org/abs/2403.16137v1
• Date: Sun, 24 Mar 2024 13:10:09 GMT
• Title: A Survey on Self-Supervised Pre-Training of Graph Foundation Models: A Knowledge-Based Perspective
• Authors: Ziwen Zhao, Yuhua Li, Yixiong Zou, Ruixuan Li, Rui Zhang,
• Abstract summary: This paper surveys and analyzes the pre-training tasks of graph foundation models from a knowledge-based perspective. It covers a total of 9 knowledge categories and 25 pre-training tasks, as well as various downstream task adaptation strategies.
• Score: 13.584118387179721
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: 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.

Related papers

• From Pixels to Insights: A Survey on Automatic Chart Understanding in the Era of Large Foundation Models [98.41645229835493]
  Data visualization in the form of charts plays a pivotal role in data analysis, offering critical insights and aiding in informed decision-making. Large foundation models, such as large language models, have revolutionized various natural language processing tasks. This survey paper serves as a comprehensive resource for researchers and practitioners in the fields of natural language processing, computer vision, and data analysis.
  arXiv  Detail & Related papers  (2024-03-18T17:57:09Z)
• UniGraph: Learning a Cross-Domain Graph Foundation Model From Natural Language [41.722898353772656]
  We present our UniGraph framework, designed to train a graph foundation model capable of generalizing to unseen graphs and tasks across diverse domains. We propose a cascaded architecture of Language Models (LMs) and Graph Neural Networks (GNNs) as backbone networks with a self-supervised training objective based on Masked Graph Modeling (MGM) Our comprehensive experiments across various graph learning tasks and domains demonstrate the model's effectiveness in self-supervised representation learning on unseen graphs, few-shot in-context transfer, and zero-shot transfer.
  arXiv  Detail & Related papers  (2024-02-21T09:06:31Z)
• A Survey of Data-Efficient Graph Learning [16.053913182723143]
  We introduce a novel concept of Data-Efficient Graph Learning (DEGL) as a research frontier. We systematically review recent advances on several key aspects, including self-supervised graph learning, semi-supervised graph learning, and few-shot graph learning.
  arXiv  Detail & Related papers  (2024-02-01T09:28:48Z)
• Towards Graph Foundation Models: A Survey and Beyond [66.37994863159861]
  Foundation models have emerged as critical components in a variety of artificial intelligence applications. The capabilities of foundation models to generalize and adapt motivate graph machine learning researchers to discuss the potential of developing a new graph learning paradigm. This article introduces the concept of Graph Foundation Models (GFMs), and offers an exhaustive explanation of their key characteristics and underlying technologies.
  arXiv  Detail & Related papers  (2023-10-18T09:31:21Z)
• Curriculum Graph Machine Learning: A Survey [51.89783017927647]
  curriculum graph machine learning (Graph CL) integrates the strength of graph machine learning and curriculum learning. This paper comprehensively overview approaches on Graph CL and present a detailed survey of recent advances in this direction.
  arXiv  Detail & Related papers  (2023-02-06T16:59:25Z)
• State of the Art and Potentialities of Graph-level Learning [54.68482109186052]
  Graph-level learning has been applied to many tasks including comparison, regression, classification, and more. Traditional approaches to learning a set of graphs rely on hand-crafted features, such as substructures. Deep learning has helped graph-level learning adapt to the growing scale of graphs by extracting features automatically and encoding graphs into low-dimensional representations.
  arXiv  Detail & Related papers  (2023-01-14T09:15:49Z)
• Graph Pooling for Graph Neural Networks: Progress, Challenges, and Opportunities [128.55790219377315]
  Graph neural networks have emerged as a leading architecture for many graph-level tasks. graph pooling is indispensable for obtaining a holistic graph-level representation of the whole graph.
  arXiv  Detail & Related papers  (2022-04-15T04:02:06Z)
• A Survey of Pretraining on Graphs: Taxonomy, Methods, and Applications [38.57023440288189]
  We provide the first comprehensive survey for Pretrained Graph Models (PGMs) We firstly present the limitations of graph representation learning and thus introduce the motivation for graph pre-training. Next, we present the applications of PGMs in social recommendation and drug discovery.
  arXiv  Detail & Related papers  (2022-02-16T07:00:52Z)
• GCC: Graph Contrastive Coding for Graph Neural Network Pre-Training [62.73470368851127]
  Graph representation learning has emerged as a powerful technique for addressing real-world problems. We design Graph Contrastive Coding -- a self-supervised graph neural network pre-training framework. We conduct experiments on three graph learning tasks and ten graph datasets.
  arXiv  Detail & Related papers  (2020-06-17T16:18:35Z)

This list is automatically generated from the titles and abstracts of the papers in this site.

This site does not guarantee the quality of this site (including all information) and is not responsible for any consequences.