Towards Graph Foundation Models: The Perspective of Zero-shot Reasoning on Knowledge Graphs

• URL: http://arxiv.org/abs/2410.12609v1
• Date: Wed, 16 Oct 2024 14:26:08 GMT
• Title: Towards Graph Foundation Models: The Perspective of Zero-shot Reasoning on Knowledge Graphs
• Authors: Kai Wang, Siqiang Luo,
• Abstract summary: We introduce SCORE, a unified graph reasoning framework that effectively generalizes diverse graph tasks using zero-shot learning. We evaluate SCORE using 38 diverse graph datasets, covering node-level, link-level, and graph-level tasks across multiple domains.
• Score: 14.392577069212292
• License:
• Abstract: Inspired by the success of artificial general intelligence, there is a trend towards developing Graph Foundation Models that excel in generalization across various graph tasks and domains. However, current models often require extensive training or fine-tuning to capture structural and semantic insights on new graphs, which limits their versatility. In this work, we explore graph foundation models from the perspective of zero-shot reasoning on Knowledge Graphs (KGs). Our focus is on utilizing KGs as a unified topological structure to tackle diverse tasks, while addressing semantic isolation challenges in KG reasoning to effectively integrate diverse semantic and structural features. This brings us new methodological insights into KG reasoning, as well as high generalizability towards foundation models in practice. Methodologically, we introduce SCORE, a unified graph reasoning framework that effectively generalizes diverse graph tasks using zero-shot learning. At the core of SCORE is semantic conditional message passing, a technique designed to capture both structural and semantic invariances in graphs, with theoretical backing for its expressive power. Practically, we evaluate the zero-shot reasoning capability of SCORE using 38 diverse graph datasets, covering node-level, link-level, and graph-level tasks across multiple domains. Our experiments reveal a substantial performance improvement over prior foundation models and supervised baselines, highlighting the efficacy and adaptability of our approach.

Related papers

• A Hierarchical Language Model For Interpretable Graph Reasoning [47.460255447561906]
  We introduce Hierarchical Language Model for Graph (HLM-G), which employs a two-block architecture to capture node-centric local information and interaction-centric global structure. The proposed scheme allows LLMs to address various graph queries with high efficacy, efficiency, and robustness, while reducing computational costs on large-scale graph tasks. Comprehensive evaluations across diverse graph reasoning and real-world tasks of node, link, and graph-levels highlight the superiority of our method.
  arXiv  Detail & Related papers  (2024-10-29T00:28:02Z)
• Foundations and Frontiers of Graph Learning Theory [81.39078977407719]
  Recent advancements in graph learning have revolutionized the way to understand and analyze data with complex structures. Graph Neural Networks (GNNs), i.e. neural network architectures designed for learning graph representations, have become a popular paradigm. This article provides a comprehensive summary of the theoretical foundations and breakthroughs concerning the approximation and learning behaviors intrinsic to prevalent graph learning models.
  arXiv  Detail & Related papers  (2024-07-03T14:07:41Z)
• 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)
• Towards Foundation Models for Knowledge Graph Reasoning [18.77355708537997]
  Knowledge graphs (KGs) have different entity and relation vocabularies that generally do not overlap. We present ULTRA, an approach for learning universal and transferable graph representations. We find that the zero-shot inductive inference performance of a single pre-trained ULTRA model on unseen graphs of various sizes is often on par or better than strong baselines trained on specific graphs.
  arXiv  Detail & Related papers  (2023-10-06T20:00:07Z)
• Group Contrastive Self-Supervised Learning on Graphs [101.45974132613293]
  We study self-supervised learning on graphs using contrastive methods. We argue that contrasting graphs in multiple subspaces enables graph encoders to capture more abundant characteristics.
  arXiv  Detail & Related papers  (2021-07-20T22:09:21Z)
• Model-Agnostic Graph Regularization for Few-Shot Learning [60.64531995451357]
  We present a comprehensive study on graph embedded few-shot learning. We introduce a graph regularization approach that allows a deeper understanding of the impact of incorporating graph information between labels. Our approach improves the performance of strong base learners by up to 2% on Mini-ImageNet and 6.7% on ImageNet-FS.
  arXiv  Detail & Related papers  (2021-02-14T05:28:13Z)
• Quantifying Challenges in the Application of Graph Representation Learning [0.0]
  We provide an application oriented perspective to a set of popular embedding approaches. We evaluate their representational power with respect to real-world graph properties. Our results suggest that "one-to-fit-all" GRL approaches are hard to define in real-world scenarios.
  arXiv  Detail & Related papers  (2020-06-18T03:19:43Z)
• 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)
• Principal Neighbourhood Aggregation for Graph Nets [4.339839287869653]
  Graph Neural Networks (GNNs) have been shown to be effective models for different predictive tasks on graph-structured data. Recent work on their expressive power has focused on isomorphism tasks and countable feature spaces. We extend this theoretical framework to include continuous features which occur regularly in real-world input domains.
  arXiv  Detail & Related papers  (2020-04-12T23:30:00Z)
• Goal-directed graph construction using reinforcement learning [3.291429094499946]
  We formulate the construction of a graph as a decision-making process in which a central agent creates topologies by trial and error. We propose an algorithm based on reinforcement learning and graph neural networks to learn graph construction and improvement strategies.
  arXiv  Detail & Related papers  (2020-01-30T12:11:45Z)

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.