AnyGraph: Graph Foundation Model in the Wild

• URL: http://arxiv.org/abs/2408.10700v1
• Date: Tue, 20 Aug 2024 09:57:13 GMT
• Title: AnyGraph: Graph Foundation Model in the Wild
• Authors: Lianghao Xia, Chao Huang,
• Abstract summary: Graph foundation models offer the potential to learn robust, generalizable representations from graph data. In this work, we investigate a unified graph model, AnyGraph, designed to handle key challenges. Our experiments on diverse 38 graph datasets have demonstrated the strong zero-shot learning performance of AnyGraph.
• Score: 16.313146933922752
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: The growing ubiquity of relational data structured as graphs has underscored the need for graph learning models with exceptional generalization capabilities. However, current approaches often struggle to effectively extract generalizable insights, frequently requiring extensive fine-tuning and limiting their versatility. Graph foundation models offer a transformative solution, with the potential to learn robust, generalizable representations from graph data. This enables more effective and adaptable applications across a wide spectrum of tasks and domains. In this work, we investigate a unified graph model, AnyGraph, designed to handle key challenges: i) Structure Heterogenity. Addressing distribution shift in graph structural information; ii) Feature Heterogenity. Handling diverse feature representation spaces across graph datasets; iii) Fast Adaptation. Efficiently adapting the model to new graph domains; iv) Scaling Law Emergence. Enabling the model to exhibit scaling law behavior, where its performance scales favorably with the amount of data and parameter sizes. To tackle these critical challenges, we build the AnyGraph upon a Graph Mixture-of-Experts (MoE) architecture. This approach empowers the model to effectively manage both the in-domain and cross-domain distribution shift concerning structure-level and feature-level heterogeneity. Furthermore, a lightweight graph expert routing mechanism is proposed to facilitate AnyGraph's fast adaptability to new data and domains. Our extensive experiments on diverse 38 graph datasets have demonstrated the strong zero-shot learning performance of AnyGraph across diverse graph domains with significant distribution shift. Furthermore, we have validated the model's fast adaptation ability and scaling law emergence, showcasing its versatility.

Related papers

• RAGraph: A General Retrieval-Augmented Graph Learning Framework [35.25522856244149]
  We introduce a novel framework called General Retrieval-Augmented Graph Learning (RAGraph) RAGraph brings external graph data into the general graph foundation model to improve model generalization on unseen scenarios. During inference, the RAGraph adeptly retrieves similar toy graphs based on key similarities in downstream tasks.
  arXiv  Detail & Related papers  (2024-10-31T12:05:21Z)
• GraphFM: A Scalable Framework for Multi-Graph Pretraining [2.882104808886318]
  We introduce a scalable multi-graph multi-task pretraining approach specifically tailored for node classification tasks across diverse graph datasets from different domains. We demonstrate the efficacy of our approach by training a model on 152 different graph datasets comprising over 7.4 million nodes and 189 million edges. Our results show that pretraining on a diverse array of real and synthetic graphs improves the model's adaptability and stability, while performing competitively with state-of-the-art specialist models.
  arXiv  Detail & Related papers  (2024-07-16T16:51:43Z)
• OpenGraph: Towards Open Graph Foundation Models [20.401374302429627]
  Graph Neural Networks (GNNs) have emerged as promising techniques for encoding structural information. Key challenge remains: the difficulty of generalizing to unseen graph data with different properties. We propose a novel graph foundation model, called OpenGraph, to address this challenge.
  arXiv  Detail & Related papers  (2024-03-02T08:05:03Z)
• GraphControl: Adding Conditional Control to Universal Graph Pre-trained Models for Graph Domain Transfer Learning [28.04023419006392]
  Graph self-supervised algorithms have achieved significant success in acquiring generic knowledge from abundant unlabeled graph data. Different graphs, even across seemingly similar domains, can differ significantly in terms of attribute semantics. We introduce an innovative deployment module coined as GraphControl, motivated by ControlNet, to realize better graph domain transfer learning.
  arXiv  Detail & Related papers  (2023-10-11T10:30:49Z)
• GraphGLOW: Universal and Generalizable Structure Learning for Graph Neural Networks [72.01829954658889]
  This paper introduces the mathematical definition of this novel problem setting. We devise a general framework that coordinates a single graph-shared structure learner and multiple graph-specific GNNs. The well-trained structure learner can directly produce adaptive structures for unseen target graphs without any fine-tuning.
  arXiv  Detail & Related papers  (2023-06-20T03:33:22Z)
• GrannGAN: Graph annotation generative adversarial networks [72.66289932625742]
  We consider the problem of modelling high-dimensional distributions and generating new examples of data with complex relational feature structure coherent with a graph skeleton. The model we propose tackles the problem of generating the data features constrained by the specific graph structure of each data point by splitting the task into two phases. In the first it models the distribution of features associated with the nodes of the given graph, in the second it complements the edge features conditionally on the node features.
  arXiv  Detail & Related papers  (2022-12-01T11:49:07Z)
• Robust Optimization as Data Augmentation for Large-scale Graphs [117.2376815614148]
  We propose FLAG (Free Large-scale Adversarial Augmentation on Graphs), which iteratively augments node features with gradient-based adversarial perturbations during training. FLAG is a general-purpose approach for graph data, which universally works in node classification, link prediction, and graph classification tasks.
  arXiv  Detail & Related papers  (2020-10-19T21:51:47Z)
• GraphOpt: Learning Optimization Models of Graph Formation [72.75384705298303]
  We propose an end-to-end framework that learns an implicit model of graph structure formation and discovers an underlying optimization mechanism. The learned objective can serve as an explanation for the observed graph properties, thereby lending itself to transfer across different graphs within a domain. GraphOpt poses link formation in graphs as a sequential decision-making process and solves it using maximum entropy inverse reinforcement learning algorithm.
  arXiv  Detail & Related papers  (2020-07-07T16:51:39Z)
• Tensor Graph Convolutional Networks for Multi-relational and Robust Learning [74.05478502080658]
  This paper introduces a tensor-graph convolutional network (TGCN) for scalable semi-supervised learning (SSL) from data associated with a collection of graphs, that are represented by a tensor. The proposed architecture achieves markedly improved performance relative to standard GCNs, copes with state-of-the-art adversarial attacks, and leads to remarkable SSL performance over protein-to-protein interaction networks.
  arXiv  Detail & Related papers  (2020-03-15T02:33:21Z)
• Adaptive Graph Auto-Encoder for General Data Clustering [90.8576971748142]
  Graph-based clustering plays an important role in the clustering area. Recent studies about graph convolution neural networks have achieved impressive success on graph type data. We propose a graph auto-encoder for general data clustering, which constructs the graph adaptively according to the generative perspective of graphs.
  arXiv  Detail & Related papers  (2020-02-20T10:11:28Z)

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.