Boosting Graph Foundation Model from Structural Perspective

• URL: http://arxiv.org/abs/2407.19941v1
• Date: Mon, 29 Jul 2024 12:22:16 GMT
• Title: Boosting Graph Foundation Model from Structural Perspective
• Authors: Yao Cheng, Yige Zhao, Jianxiang Yu, Xiang Li,
• Abstract summary: We boost graph foundation model from structural perspective and propose BooG. BooG constructs virtual super nodes to unify structural characteristics of graph data from different domains. We also propose a novel pre-training objective based on contrastive learning, which learns more expressive representations for graph data.
• Score: 6.387816922598151
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: Graph foundation models have recently attracted significant attention due to its strong generalizability. Although existing methods resort to language models to learn unified semantic representations across domains, they disregard the unique structural characteristics of graphs from different domains. To address the problem, in this paper, we boost graph foundation model from structural perspective and propose BooG. The model constructs virtual super nodes to unify structural characteristics of graph data from different domains. Specifically, the super nodes fuse the information of anchor nodes and class labels, where each anchor node captures the information of a node or a graph instance to be classified. Instead of using the raw graph structure, we connect super nodes to all nodes within their neighborhood by virtual edges. This new structure allows for effective information aggregation while unifying cross-domain structural characteristics. Additionally, we propose a novel pre-training objective based on contrastive learning, which learns more expressive representations for graph data and generalizes effectively to different domains and downstream tasks. Experimental results on various datasets and tasks demonstrate the superior performance of BooG. We provide our code and data here: https://anonymous.4open.science/r/BooG-EE42/.

Related papers

• Node-like as a Whole: Structure-aware Searching and Coarsening for Graph Classification [14.602474387096244]
  Graph Transformers (GTs) have made remarkable achievements in graph-level tasks. Can we treat graph structures node-like as a whole to learn high-level features? We propose a novel multi-view graph representation learning model via structure-aware searching and coarsening.
  arXiv  Detail & Related papers  (2024-04-18T03:03:37Z)
• Incorporating Higher-order Structural Information for Graph Clustering [6.027366081402081]
  Graph convolutional network (GCN) has emerged as a powerful tool for deep clustering. We propose a novel graph clustering network to make full use of graph structural information. Our proposed model outperforms many state-of-the-art methods on various datasets.
  arXiv  Detail & Related papers  (2024-03-17T04:42:41Z)
• 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)
• GraphEdit: Large Language Models for Graph Structure Learning [62.618818029177355]
  Graph Structure Learning (GSL) focuses on capturing intrinsic dependencies and interactions among nodes in graph-structured data. Existing GSL methods heavily depend on explicit graph structural information as supervision signals. We propose GraphEdit, an approach that leverages large language models (LLMs) to learn complex node relationships in graph-structured data.
  arXiv  Detail & Related papers  (2024-02-23T08:29:42Z)
• 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)
• A Robust Stacking Framework for Training Deep Graph Models with Multifaceted Node Features [61.92791503017341]
  Graph Neural Networks (GNNs) with numerical node features and graph structure as inputs have demonstrated superior performance on various supervised learning tasks with graph data. The best models for such data types in most standard supervised learning settings with IID (non-graph) data are not easily incorporated into a GNN. Here we propose a robust stacking framework that fuses graph-aware propagation with arbitrary models intended for IID data.
  arXiv  Detail & Related papers  (2022-06-16T22:46:33Z)
• SHGNN: Structure-Aware Heterogeneous Graph Neural Network [77.78459918119536]
  This paper proposes a novel Structure-Aware Heterogeneous Graph Neural Network (SHGNN) to address the above limitations. We first utilize a feature propagation module to capture the local structure information of intermediate nodes in the meta-path. Next, we use a tree-attention aggregator to incorporate the graph structure information into the aggregation module on the meta-path. Finally, we leverage a meta-path aggregator to fuse the information aggregated from different meta-paths.
  arXiv  Detail & Related papers  (2021-12-12T14:18:18Z)
• Graph Information Bottleneck [77.21967740646784]
  Graph Neural Networks (GNNs) provide an expressive way to fuse information from network structure and node features. Inheriting from the general Information Bottleneck (IB), GIB aims to learn the minimal sufficient representation for a given task. We show that our proposed models are more robust than state-of-the-art graph defense models.
  arXiv  Detail & Related papers  (2020-10-24T07:13:00Z)
• Graph InfoClust: Leveraging cluster-level node information for unsupervised graph representation learning [12.592903558338444]
  We propose a graph representation learning method called Graph InfoClust. It seeks to additionally capture cluster-level information content. This optimization leads the node representations to capture richer information and nodal interactions, which improves their quality.
  arXiv  Detail & Related papers  (2020-09-15T09:33:20Z)
• CatGCN: Graph Convolutional Networks with Categorical Node Features [99.555850712725]
  CatGCN is tailored for graph learning when the node features are categorical. We train CatGCN in an end-to-end fashion and demonstrate it on semi-supervised node classification.
  arXiv  Detail & Related papers  (2020-09-11T09:25:17Z)

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.