Hi-GMAE: Hierarchical Graph Masked Autoencoders

• URL: http://arxiv.org/abs/2405.10642v1
• Date: Fri, 17 May 2024 09:08:37 GMT
• Title: Hi-GMAE: Hierarchical Graph Masked Autoencoders
• Authors: Chuang Liu, Zelin Yao, Yibing Zhan, Xueqi Ma, Dapeng Tao, Jia Wu, Wenbin Hu, Shirui Pan, Bo Du,
• Abstract summary: Hierarchical Graph Masked AutoEncoders (Hi-GMAE) Hi-GMAE is a novel multi-scale GMAE framework designed to handle the hierarchical structures within graphs. Our experiments on 15 graph datasets consistently demonstrate that Hi-GMAE outperforms 17 state-of-the-art self-supervised competitors.
• Score: 90.30572554544385
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: Graph Masked Autoencoders (GMAEs) have emerged as a notable self-supervised learning approach for graph-structured data. Existing GMAE models primarily focus on reconstructing node-level information, categorizing them as single-scale GMAEs. This methodology, while effective in certain contexts, tends to overlook the complex hierarchical structures inherent in many real-world graphs. For instance, molecular graphs exhibit a clear hierarchical organization in the form of the atoms-functional groups-molecules structure. Hence, the inability of single-scale GMAE models to incorporate these hierarchical relationships often leads to their inadequate capture of crucial high-level graph information, resulting in a noticeable decline in performance. To address this limitation, we propose Hierarchical Graph Masked AutoEncoders (Hi-GMAE), a novel multi-scale GMAE framework designed to handle the hierarchical structures within graphs. First, Hi-GMAE constructs a multi-scale graph hierarchy through graph pooling, enabling the exploration of graph structures across different granularity levels. To ensure masking uniformity of subgraphs across these scales, we propose a novel coarse-to-fine strategy that initiates masking at the coarsest scale and progressively back-projects the mask to the finer scales. Furthermore, we integrate a gradual recovery strategy with the masking process to mitigate the learning challenges posed by completely masked subgraphs. Diverging from the standard graph neural network (GNN) used in GMAE models, Hi-GMAE modifies its encoder and decoder into hierarchical structures. This entails using GNN at the finer scales for detailed local graph analysis and employing a graph transformer at coarser scales to capture global information. Our experiments on 15 graph datasets consistently demonstrate that Hi-GMAE outperforms 17 state-of-the-art self-supervised competitors.

Related papers

• GraphCroc: Cross-Correlation Autoencoder for Graph Structural Reconstruction [6.817416560637197]
  Graph autoencoders (GAEs) reconstruct graph structures from node embeddings. We introduce a cross-correlation mechanism that significantly enhances the GAE representational capabilities. We also propose GraphCroc, a new GAE that supports flexible encoder architectures tailored for various downstream tasks.
  arXiv  Detail & Related papers  (2024-10-04T12:59:45Z)
• Informative Subgraphs Aware Masked Auto-Encoder in Dynamic Graphs [1.3571543090749625]
  We introduce a constrained probabilistic generative model to generate informative subgraphs that guide the evolution of dynamic graphs. The informative subgraph identified by DyGIS will serve as the input of dynamic graph masked autoencoder (DGMAE)
  arXiv  Detail & Related papers  (2024-09-14T02:16:00Z)
• Generative and Contrastive Paradigms Are Complementary for Graph Self-Supervised Learning [56.45977379288308]
  Masked autoencoder (MAE) learns to reconstruct masked graph edges or node features. Contrastive Learning (CL) maximizes the similarity between augmented views of the same graph. We propose graph contrastive masked autoencoder (GCMAE) framework to unify MAE and CL.
  arXiv  Detail & Related papers  (2023-10-24T05:06:06Z)
• Transforming Graphs for Enhanced Attribute Clustering: An Innovative Graph Transformer-Based Method [8.989218350080844]
  This study introduces an innovative method known as the Graph Transformer Auto-Encoder for Graph Clustering (GTAGC) By melding the Graph Auto-Encoder with the Graph Transformer, GTAGC is adept at capturing global dependencies between nodes. The architecture of GTAGC encompasses graph embedding, integration of the Graph Transformer within the autoencoder structure, and a clustering component.
  arXiv  Detail & Related papers  (2023-06-20T06:04:03Z)
• Towards Unsupervised Deep Graph Structure Learning [67.58720734177325]
  We propose an unsupervised graph structure learning paradigm, where the learned graph topology is optimized by data itself without any external guidance. Specifically, we generate a learning target from the original data as an "anchor graph", and use a contrastive loss to maximize the agreement between the anchor graph and the learned graph.
  arXiv  Detail & Related papers  (2022-01-17T11:57:29Z)
• MGAE: Masked Autoencoders for Self-Supervised Learning on Graphs [55.66953093401889]
  Masked graph autoencoder (MGAE) framework to perform effective learning on graph structure data. Taking insights from self-supervised learning, we randomly mask a large proportion of edges and try to reconstruct these missing edges during training.
  arXiv  Detail & Related papers  (2022-01-07T16:48:07Z)
• Self-supervised Graph-level Representation Learning with Local and Global Structure [71.45196938842608]
  We propose a unified framework called Local-instance and Global-semantic Learning (GraphLoG) for self-supervised whole-graph representation learning. Besides preserving the local similarities, GraphLoG introduces the hierarchical prototypes to capture the global semantic clusters. An efficient online expectation-maximization (EM) algorithm is further developed for learning the model.
  arXiv  Detail & Related papers  (2021-06-08T05:25:38Z)
• 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.