Towards Real-Time Temporal Graph Learning

• URL: http://arxiv.org/abs/2210.04114v2
• Date: Wed, 12 Oct 2022 03:36:08 GMT
• Title: Towards Real-Time Temporal Graph Learning
• Authors: Deniz Gurevin, Mohsin Shan, Tong Geng, Weiwen Jiang, Caiwen Ding and Omer Khan
• Abstract summary: 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.
• Score: 10.647431919265346
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: In recent years, graph representation learning has gained significant popularity, which aims to generate node embeddings that capture features of graphs. One of the methods to achieve this is employing a technique called random walks that captures node sequences in a graph and then learns embeddings for each node using a natural language processing technique called Word2Vec. These embeddings are then used for deep learning on graph data for classification tasks, such as link prediction or node classification. Prior work operates on pre-collected temporal graph data and is not designed to handle updates on a graph in real-time. Real world graphs change dynamically and their entire temporal updates are not available upfront. In this paper, we propose an end-to-end graph learning pipeline that performs temporal graph construction, creates low-dimensional node embeddings, and trains multi-layer neural network models in an online setting. The training of the neural network models is identified as the main performance bottleneck as it performs repeated matrix operations on many sequentially connected low-dimensional kernels. We propose to unlock fine-grain parallelism in these low-dimensional kernels to boost performance of model training.

Related papers

• Learning on Large Graphs using Intersecting Communities [13.053266613831447]
  MPNNs iteratively update each node's representation in an input graph by aggregating messages from the node's neighbors. MPNNs might quickly become prohibitive for large graphs provided they are not very sparse. We propose approximating the input graph as an intersecting community graph (ICG) -- a combination of intersecting cliques.
  arXiv  Detail & Related papers  (2024-05-31T09:26:26Z)
• Dynamic Graph Message Passing Networks for Visual Recognition [112.49513303433606]
  Modelling long-range dependencies is critical for scene understanding tasks in computer vision. A fully-connected graph is beneficial for such modelling, but its computational overhead is prohibitive. We propose a dynamic graph message passing network, that significantly reduces the computational complexity.
  arXiv  Detail & Related papers  (2022-09-20T14:41:37Z)
• 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)
• Neural Graph Matching for Pre-training Graph Neural Networks [72.32801428070749]
  Graph neural networks (GNNs) have been shown powerful capacity at modeling structural data. We present a novel Graph Matching based GNN Pre-Training framework, called GMPT. The proposed method can be applied to fully self-supervised pre-training and coarse-grained supervised pre-training.
  arXiv  Detail & Related papers  (2022-03-03T09:53:53Z)
• Graph Kernel Neural Networks [53.91024360329517]
  We propose to use graph kernels, i.e. kernel functions that compute an inner product on graphs, to extend the standard convolution operator to the graph domain. This allows us to define an entirely structural model that does not require computing the embedding of the input graph. Our architecture allows to plug-in any type of graph kernels and has the added benefit of providing some interpretability.
  arXiv  Detail & Related papers  (2021-12-14T14:48:08Z)
• Temporal Graph Network Embedding with Causal Anonymous Walks Representations [54.05212871508062]
  We propose a novel approach for dynamic network representation learning based on Temporal Graph Network. For evaluation, we provide a benchmark pipeline for the evaluation of temporal network embeddings. We show the applicability and superior performance of our model in the real-world downstream graph machine learning task provided by one of the top European banks.
  arXiv  Detail & Related papers  (2021-08-19T15:39:52Z)
• Learning Graph Representations [0.0]
  Graph Neural Networks (GNNs) are efficient ways to get insight into large dynamic graph datasets. In this paper, we discuss the graph convolutional neural networks graph autoencoders and Social-temporal graph neural networks.
  arXiv  Detail & Related papers  (2021-02-03T12:07:55Z)
• Co-embedding of Nodes and Edges with Graph Neural Networks [13.020745622327894]
  Graph embedding is a way to transform and encode the data structure in high dimensional and non-Euclidean feature space. CensNet is a general graph embedding framework, which embeds both nodes and edges to a latent feature space. Our approach achieves or matches the state-of-the-art performance in four graph learning tasks.
  arXiv  Detail & Related papers  (2020-10-25T22:39:31Z)
• Line Graph Neural Networks for Link Prediction [71.00689542259052]
  We consider the graph link prediction task, which is a classic graph analytical problem with many real-world applications. In this formalism, a link prediction problem is converted to a graph classification task. We propose to seek a radically different and novel path by making use of the line graphs in graph theory. In particular, each node in a line graph corresponds to a unique edge in the original graph. Therefore, link prediction problems in the original graph can be equivalently solved as a node classification problem in its corresponding line graph, instead of a graph classification task.
  arXiv  Detail & Related papers  (2020-10-20T05:54:31Z)
• Lifelong Graph Learning [6.282881904019272]
  We bridge graph learning and lifelong learning by converting a continual graph learning problem to a regular graph learning problem. We show that feature graph networks (FGN) achieve superior performance in two applications, i.e., lifelong human action recognition with wearable devices and feature matching.
  arXiv  Detail & Related papers  (2020-09-01T18:21:34Z)
• Time-varying Graph Representation Learning via Higher-Order Skip-Gram with Negative Sampling [0.456877715768796]
  We build upon the fact that the skip-gram embedding approach implicitly performs a matrix factorization. We show that higher-order skip-gram with negative sampling is able to disentangle the role of nodes and time. We empirically evaluate our approach using time-resolved face-to-face proximity data, showing that the learned time-varying graph representations outperform state-of-the-art methods.
  arXiv  Detail & Related papers  (2020-06-25T12:04:48Z)

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.