HGWaveNet: A Hyperbolic Graph Neural Network for Temporal Link Prediction

• URL: http://arxiv.org/abs/2304.07302v2
• Date: Wed, 3 May 2023 04:54:52 GMT
• Title: HGWaveNet: A Hyperbolic Graph Neural Network for Temporal Link Prediction
• Authors: Qijie Bai, Changli Nie, Haiwei Zhang, Dongming Zhao, Xiaojie Yuan
• Abstract summary: We propose HGWaveNet, a novel hyperbolic graph neural network that fully exploits the fitness between hyperbolic spaces and data distributions for temporal link prediction. Specifically, we design two key modules to learn the spatial topological structures and temporal evolutionary information separately. The results show a relative improvement by up to 6.67% on AUC for temporal link prediction over SOTA methods.
• Score: 9.110162634132827
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: Temporal link prediction, aiming to predict future edges between paired nodes in a dynamic graph, is of vital importance in diverse applications. However, existing methods are mainly built upon uniform Euclidean space, which has been found to be conflict with the power-law distributions of real-world graphs and unable to represent the hierarchical connections between nodes effectively. With respect to the special data characteristic, hyperbolic geometry offers an ideal alternative due to its exponential expansion property. In this paper, we propose HGWaveNet, a novel hyperbolic graph neural network that fully exploits the fitness between hyperbolic spaces and data distributions for temporal link prediction. Specifically, we design two key modules to learn the spatial topological structures and temporal evolutionary information separately. On the one hand, a hyperbolic diffusion graph convolution (HDGC) module effectively aggregates information from a wider range of neighbors. On the other hand, the internal order of causal correlation between historical states is captured by hyperbolic dilated causal convolution (HDCC) modules. The whole model is built upon the hyperbolic spaces to preserve the hierarchical structural information in the entire data flow. To prove the superiority of HGWaveNet, extensive experiments are conducted on six real-world graph datasets and the results show a relative improvement by up to 6.67% on AUC for temporal link prediction over SOTA methods.

Related papers

• Hyperbolic Graph Diffusion Model [24.049660417511074]
  We propose a novel graph generation method called, Hyperbolic Graph Diffusion Model (HGDM) HGDM consists of an auto-encoder to encode nodes into successive hyperbolic embeddings, and a DM that operates in the hyperbolic latent space. Experiments show that HGDM achieves better performance in generic graph and molecule generation benchmarks, with a $48%$ improvement in the quality of graph generation with highly hierarchical structures.
  arXiv  Detail & Related papers  (2023-06-13T08:22:18Z)
• 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)
• Temporal Aggregation and Propagation Graph Neural Networks for Dynamic Representation [67.26422477327179]
  Temporal graphs exhibit dynamic interactions between nodes over continuous time. We propose a novel method of temporal graph convolution with the whole neighborhood. Our proposed TAP-GNN outperforms existing temporal graph methods by a large margin in terms of both predictive performance and online inference latency.
  arXiv  Detail & Related papers  (2023-04-15T08:17:18Z)
• Spatial-Temporal Adaptive Graph Convolution with Attention Network for Traffic Forecasting [4.1700160312787125]
  We propose a novel network, Spatial-Temporal Adaptive graph convolution with Attention Network (STAAN) for traffic forecasting. Firstly, we adopt an adaptive dependency matrix instead of using a pre-defined matrix during GCN processing to infer the inter-dependencies among nodes. Secondly, we integrate PW-attention based on graph attention network which is designed for global dependency, and GCN as spatial block.
  arXiv  Detail & Related papers  (2022-06-07T09:08:35Z)
• Spatio-Temporal Latent Graph Structure Learning for Traffic Forecasting [6.428566223253948]
  We propose a new traffic forecasting framework--S-Temporal Latent Graph Structure Learning networks (ST-LGSL) The model employs a graph based on Multilayer perceptron and K-Nearest Neighbor, which learns the latent graph topological information from the entire data. With the dependencies-kNN based on ground-truth adjacency matrix and similarity metric in kNN, ST-LGSL aggregates the top focusing on geography and node similarity.
  arXiv  Detail & Related papers  (2022-02-25T10:02:49Z)
• ACE-HGNN: Adaptive Curvature Exploration Hyperbolic Graph Neural Network [72.16255675586089]
  We propose an Adaptive Curvature Exploration Hyperbolic Graph NeuralNetwork named ACE-HGNN to adaptively learn the optimal curvature according to the input graph and downstream tasks. Experiments on multiple real-world graph datasets demonstrate a significant and consistent performance improvement in model quality with competitive performance and good generalization ability.
  arXiv  Detail & Related papers  (2021-10-15T07:18:57Z)
• Discrete-time Temporal Network Embedding via Implicit Hierarchical Learning in Hyperbolic Space [43.280123606888395]
  We propose a hyperbolic temporal graph network (HTGN) that takes advantage of the exponential capacity and hierarchical awareness of hyperbolic geometry. HTGN maps the temporal graph into hyperbolic space, and incorporates hyperbolic graph neural network and hyperbolic gated recurrent neural network. Experimental results on multiple real-world datasets demonstrate the superiority of HTGN for temporal graph embedding.
  arXiv  Detail & Related papers  (2021-07-08T11:24:59Z)
• Predicting traffic signals on transportation networks using spatio-temporal correlations on graphs [56.48498624951417]
  This paper proposes a traffic propagation model that merges multiple heat diffusion kernels into a data-driven prediction model to forecast traffic signals. We optimize the model parameters using Bayesian inference to minimize the prediction errors and, consequently, determine the mixing ratio of the two approaches. The proposed model demonstrates prediction accuracy comparable to that of the state-of-the-art deep neural networks with lower computational effort.
  arXiv  Detail & Related papers  (2021-04-27T18:17:42Z)
• Hyperbolic Variational Graph Neural Network for Modeling Dynamic Graphs [77.33781731432163]
  We learn dynamic graph representation in hyperbolic space, for the first time, which aims to infer node representations. We present a novel Hyperbolic Variational Graph Network, referred to as HVGNN. In particular, to model the dynamics, we introduce a Temporal GNN (TGNN) based on a theoretically grounded time encoding approach.
  arXiv  Detail & Related papers  (2021-04-06T01:44:15Z)
• Block-Approximated Exponential Random Graphs [77.4792558024487]
  An important challenge in the field of exponential random graphs (ERGs) is the fitting of non-trivial ERGs on large graphs. We propose an approximative framework to such non-trivial ERGs that result in dyadic independence (i.e., edge independent) distributions. Our methods are scalable to sparse graphs consisting of millions of nodes.
  arXiv  Detail & Related papers  (2020-02-14T11:42:16Z)

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.