DyGCN: Dynamic Graph Embedding with Graph Convolutional Network

• URL: http://arxiv.org/abs/2104.02962v1
• Date: Wed, 7 Apr 2021 07:28:44 GMT
• Title: DyGCN: Dynamic Graph Embedding with Graph Convolutional Network
• Authors: Zeyu Cui, Zekun Li, Shu Wu, Xiaoyu Zhang, Qiang Liu, Liang Wang, Mengmeng Ai
• Abstract summary: We propose an efficient dynamic graph embedding approach, Dynamic Graph Convolutional Network (DyGCN) Our model can update the node embeddings in a time-saving and performance-preserving way.
• Score: 25.02329024926518
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: Graph embedding, aiming to learn low-dimensional representations (aka. embeddings) of nodes, has received significant attention recently. Recent years have witnessed a surge of efforts made on static graphs, among which Graph Convolutional Network (GCN) has emerged as an effective class of models. However, these methods mainly focus on the static graph embedding. In this work, we propose an efficient dynamic graph embedding approach, Dynamic Graph Convolutional Network (DyGCN), which is an extension of GCN-based methods. We naturally generalizes the embedding propagation scheme of GCN to dynamic setting in an efficient manner, which is to propagate the change along the graph to update node embeddings. The most affected nodes are first updated, and then their changes are propagated to the further nodes and leads to their update. Extensive experiments conducted on various dynamic graphs demonstrate that our model can update the node embeddings in a time-saving and performance-preserving way.

Related papers

• Gradient Transformation: Towards Efficient and Model-Agnostic Unlearning for Dynamic Graph Neural Networks [66.70786325911124]
  Graph unlearning has emerged as an essential tool for safeguarding user privacy and mitigating the negative impacts of undesirable data. With the increasing prevalence of DGNNs, it becomes imperative to investigate the implementation of dynamic graph unlearning. We propose an effective, efficient, model-agnostic, and post-processing method to implement DGNN unlearning.
  arXiv  Detail & Related papers  (2024-05-23T10:26:18Z)
• Dynamic Graph Node Classification via Time Augmentation [15.580277876084873]
  We propose the Time Augmented Graph Dynamic Neural Network (TADGNN) framework for node classification on dynamic graphs. TADGNN consists of two modules: 1) a time augmentation module that captures the temporal evolution of nodes across time structurally, creating a time-augmentedtemporal graph, and 2) an information propagation module that learns the dynamic representations for each node across time using the constructed time-augmented graph. Experimental results demonstrate that TADGNN framework outperforms several static and dynamic state-of-the-art (SOTA) GNN models while demonstrating superior scalability.
  arXiv  Detail & Related papers  (2022-12-07T04:13:23Z)
• Training Graph Neural Networks on Growing Stochastic Graphs [114.75710379125412]
  Graph Neural Networks (GNNs) rely on graph convolutions to exploit meaningful patterns in networked data. We propose to learn GNNs on very large graphs by leveraging the limit object of a sequence of growing graphs, the graphon.
  arXiv  Detail & Related papers  (2022-10-27T16:00:45Z)
• Towards Real-Time Temporal Graph Learning [10.647431919265346]
  We propose an end-to-end graph learning pipeline that performs temporal graph construction, creates low-dimensional node embeddings, and trains neural network models in an online setting.
  arXiv  Detail & Related papers  (2022-10-08T22:14:31Z)
• ROLAND: Graph Learning Framework for Dynamic Graphs [75.96510058864463]
  Graph Neural Networks (GNNs) have been successfully applied to many real-world static graphs. Existing dynamic GNNs do not incorporate state-of-the-art designs from static GNNs. We propose ROLAND, an effective graph representation learning framework for real-world dynamic graphs.
  arXiv  Detail & Related papers  (2022-08-15T14:51:47Z)
• Instant Graph Neural Networks for Dynamic Graphs [18.916632816065935]
  We propose Instant Graph Neural Network (InstantGNN), an incremental approach for the graph representation matrix of dynamic graphs. Our method avoids time-consuming, repetitive computations and allows instant updates on the representation and instant predictions. Our model achieves state-of-the-art accuracy while having orders-of-magnitude higher efficiency than existing methods.
  arXiv  Detail & Related papers  (2022-06-03T03:27:42Z)
• DOTIN: Dropping Task-Irrelevant Nodes for GNNs [119.17997089267124]
  Recent graph learning approaches have introduced the pooling strategy to reduce the size of graphs for learning. We design a new approach called DOTIN (underlineDrunderlineopping underlineTask-underlineIrrelevant underlineNodes) to reduce the size of graphs. Our method speeds up GAT by about 50% on graph-level tasks including graph classification and graph edit distance.
  arXiv  Detail & Related papers  (2022-04-28T12:00:39Z)
• GCN-SE: Attention as Explainability for Node Classification in Dynamic Graphs [20.330666300034338]
  Graph Convolutional Networks (GCNs) are a popular method from graph representation learning. We propose a new method, GCN-SE, that attaches a set of learnable attention weights to graph snapshots at different times. We show that GCN-SE outperforms previously proposed node classification methods on a variety of graph datasets.
  arXiv  Detail & Related papers  (2021-10-11T20:30:35Z)
• Dirichlet Graph Variational Autoencoder [65.94744123832338]
  We present Dirichlet Graph Variational Autoencoder (DGVAE) with graph cluster memberships as latent factors. Motivated by the low pass characteristics in balanced graph cut, we propose a new variant of GNN named Heatts to encode the input graph into cluster memberships.
  arXiv  Detail & Related papers  (2020-10-09T07:35:26Z)
• 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)
• K-Core based Temporal Graph Convolutional Network for Dynamic Graphs [19.237377882738063]
  We propose a novel k-core based temporal graph convolutional network, the CTGCN, to learn node representations for dynamic graphs. In contrast to previous dynamic graph embedding methods, CTGCN can preserve both local connective proximity and global structural similarity. Experimental results on 7 real-world graphs demonstrate that the CTGCN outperforms existing state-of-the-art graph embedding methods in several tasks.
  arXiv  Detail & Related papers  (2020-03-22T14:15:27Z)

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.