EvoNet: A Neural Network for Predicting the Evolution of Dynamic Graphs

• URL: http://arxiv.org/abs/2003.00842v1
• Date: Mon, 2 Mar 2020 12:59:05 GMT
• Title: EvoNet: A Neural Network for Predicting the Evolution of Dynamic Graphs
• Authors: Changmin Wu, Giannis Nikolentzos, Michalis Vazirgiannis
• Abstract summary: We propose a model that predicts the evolution of dynamic graphs. Specifically, we use a graph neural network along with a recurrent architecture to capture the temporal evolution patterns of dynamic graphs. We evaluate the proposed model on several artificial datasets following common network evolving dynamics, as well as on real-world datasets.
• Score: 26.77596449192451
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: Neural networks for structured data like graphs have been studied extensively in recent years. To date, the bulk of research activity has focused mainly on static graphs. However, most real-world networks are dynamic since their topology tends to change over time. Predicting the evolution of dynamic graphs is a task of high significance in the area of graph mining. Despite its practical importance, the task has not been explored in depth so far, mainly due to its challenging nature. In this paper, we propose a model that predicts the evolution of dynamic graphs. Specifically, we use a graph neural network along with a recurrent architecture to capture the temporal evolution patterns of dynamic graphs. Then, we employ a generative model which predicts the topology of the graph at the next time step and constructs a graph instance that corresponds to that topology. We evaluate the proposed model on several artificial datasets following common network evolving dynamics, as well as on real-world datasets. Results demonstrate the effectiveness of the proposed model.

Related papers

• Node-Time Conditional Prompt Learning In Dynamic Graphs [14.62182210205324]
  We propose DYGPROMPT, a novel pre-training and prompt learning framework for dynamic graph modeling. We recognize that node and time features mutually characterize each other, and propose dual condition-nets to model the evolving node-time patterns in downstream tasks.
  arXiv  Detail & Related papers  (2024-05-22T19:10:24Z)
• A survey of dynamic graph neural networks [26.162035361191805]
  Graph neural networks (GNNs) have emerged as a powerful tool for effectively mining and learning from graph-structured data. This paper provides a comprehensive review of the fundamental concepts, key techniques, and state-of-the-art dynamic GNN models.
  arXiv  Detail & Related papers  (2024-04-28T15:07:48Z)
• Dynamic Causal Explanation Based Diffusion-Variational Graph Neural Network for Spatio-temporal Forecasting [60.03169701753824]
  We propose a novel Dynamic Diffusion-al Graph Neural Network (DVGNN) fortemporal forecasting. The proposed DVGNN model outperforms state-of-the-art approaches and achieves outstanding Root Mean Squared Error result.
  arXiv  Detail & Related papers  (2023-05-16T11:38:19Z)
• Dynamic Graph Representation Learning with Neural Networks: A Survey [0.0]
  Dynamic graph representations have emerged as a new machine learning problem. This paper aims at providing a review of problems and models related to dynamic graph learning.
  arXiv  Detail & Related papers  (2023-04-12T09:39:17Z)
• Learning Graph Structure from Convolutional Mixtures [119.45320143101381]
  We propose a graph convolutional relationship between the observed and latent graphs, and formulate the graph learning task as a network inverse (deconvolution) problem. In lieu of eigendecomposition-based spectral methods, we unroll and truncate proximal gradient iterations to arrive at a parameterized neural network architecture that we call a Graph Deconvolution Network (GDN) GDNs can learn a distribution of graphs in a supervised fashion, perform link prediction or edge-weight regression tasks by adapting the loss function, and they are inherently inductive.
  arXiv  Detail & Related papers  (2022-05-19T14:08:15Z)
• Hyperbolic Graph Neural Networks: A Review of Methods and Applications [55.5502008501764]
  Graph neural networks generalize conventional neural networks to graph-structured data. The performance of Euclidean models in graph-related learning is still bounded and limited by the representation ability of Euclidean geometry. Recently, hyperbolic space has gained increasing popularity in processing graph data with tree-like structure and power-law distribution.
  arXiv  Detail & Related papers  (2022-02-28T15:08:48Z)
• Learning to Evolve on Dynamic Graphs [5.1521870302904125]
  Learning to Evolve on Dynamic Graphs (LEDG) is a novel algorithm that jointly learns graph information and time information. LEDG is model-agnostic and can train any message passing based graph neural network (GNN) on dynamic graphs.
  arXiv  Detail & Related papers  (2021-11-13T04:09:30Z)
• TCL: Transformer-based Dynamic Graph Modelling via Contrastive Learning [87.38675639186405]
  We propose a novel graph neural network approach, called TCL, which deals with the dynamically-evolving graph in a continuous-time fashion. To the best of our knowledge, this is the first attempt to apply contrastive learning to representation learning on dynamic graphs.
  arXiv  Detail & Related papers  (2021-05-17T15:33:25Z)
• Pre-Training on Dynamic Graph Neural Networks [26.139844652756334]
  This paper proposes a pre-training method on dynamic graph neural networks (PT-DGNN) It uses dynamic attributed graph generation tasks to simultaneously learn the structure, semantics, and evolution features of the graph.
  arXiv  Detail & Related papers  (2021-02-24T16:06:32Z)
• Towards Deeper Graph Neural Networks [63.46470695525957]
  Graph convolutions perform neighborhood aggregation and represent one of the most important graph operations. Several recent studies attribute this performance deterioration to the over-smoothing issue. We propose Deep Adaptive Graph Neural Network (DAGNN) to adaptively incorporate information from large receptive fields.
  arXiv  Detail & Related papers  (2020-07-18T01:11:14Z)
• Deep Learning for Learning Graph Representations [58.649784596090385]
  Mining graph data has become a popular research topic in computer science. The huge amount of network data has posed great challenges for efficient analysis. This motivates the advent of graph representation which maps the graph into a low-dimension vector space.
  arXiv  Detail & Related papers  (2020-01-02T02:13:28Z)

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.