Scalable Graph Embedding LearningOn A Single GPU

• URL: http://arxiv.org/abs/2110.06991v1
• Date: Wed, 13 Oct 2021 19:09:33 GMT
• Title: Scalable Graph Embedding LearningOn A Single GPU
• Authors: Azita Nouri, Philip E. Davis, Pradeep Subedi, Manish Parashar
• Abstract summary: We introduce a hybrid CPU-GPU framework that addresses the challenges of learning embedding of large-scale graphs. We show that our system can scale training to datasets with an order of magnitude greater than a single machine's total memory capacity.
• Score: 18.142879223260785
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: Graph embedding techniques have attracted growing interest since they convert the graph data into continuous and low-dimensional space. Effective graph analytic provides users a deeper understanding of what is behind the data and thus can benefit a variety of machine learning tasks. With the current scale of real-world applications, most graph analytic methods suffer high computation and space costs. These methods and systems can process a network with thousands to a few million nodes. However, scaling to large-scale networks remains a challenge. The complexity of training graph embedding system requires the use of existing accelerators such as GPU. In this paper, we introduce a hybrid CPU-GPU framework that addresses the challenges of learning embedding of large-scale graphs. The performance of our method is compared qualitatively and quantitatively with the existing embedding systems on common benchmarks. We also show that our system can scale training to datasets with an order of magnitude greater than a single machine's total memory capacity. The effectiveness of the learned embedding is evaluated within multiple downstream applications. The experimental results indicate the effectiveness of the learned embedding in terms of performance and accuracy.

Related papers

• State of the Art and Potentialities of Graph-level Learning [54.68482109186052]
  Graph-level learning has been applied to many tasks including comparison, regression, classification, and more. Traditional approaches to learning a set of graphs rely on hand-crafted features, such as substructures. Deep learning has helped graph-level learning adapt to the growing scale of graphs by extracting features automatically and encoding graphs into low-dimensional representations.
  arXiv  Detail & Related papers  (2023-01-14T09:15:49Z)
• Localized Contrastive Learning on Graphs [110.54606263711385]
  We introduce a simple yet effective contrastive model named Localized Graph Contrastive Learning (Local-GCL) In spite of its simplicity, Local-GCL achieves quite competitive performance in self-supervised node representation learning tasks on graphs with various scales and properties.
  arXiv  Detail & Related papers  (2022-12-08T23:36:00Z)
• A Comprehensive Study on Large-Scale Graph Training: Benchmarking and Rethinking [124.21408098724551]
  Large-scale graph training is a notoriously challenging problem for graph neural networks (GNNs) We present a new ensembling training manner, named EnGCN, to address the existing issues. Our proposed method has achieved new state-of-the-art (SOTA) performance on large-scale datasets.
  arXiv  Detail & Related papers  (2022-10-14T03:43:05Z)
• Scaling R-GCN Training with Graph Summarization [71.06855946732296]
  Training of Relation Graph Convolutional Networks (R-GCN) does not scale well with the size of the graph. In this work, we experiment with the use of graph summarization techniques to compress the graph. We obtain reasonable results on the AIFB, MUTAG and AM datasets.
  arXiv  Detail & Related papers  (2022-03-05T00:28:43Z)
• Learning Massive Graph Embeddings on a Single Machine [11.949017733445624]
  A graph embedding is a fixed length vector representation for each node (and/or edge-type) in a graph. Current systems for learning the embeddings of large-scale graphs are bottlenecked by data movement. We propose Gaius, a system for efficient training of graph embeddings.
  arXiv  Detail & Related papers  (2021-01-20T23:17:31Z)
• Distributed Training of Graph Convolutional Networks using Subgraph Approximation [72.89940126490715]
  We propose a training strategy that mitigates the lost information across multiple partitions of a graph through a subgraph approximation scheme. The subgraph approximation approach helps the distributed training system converge at single-machine accuracy.
  arXiv  Detail & Related papers  (2020-12-09T09:23:49Z)
• 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)
• Not Half Bad: Exploring Half-Precision in Graph Convolutional Neural Networks [8.460826851547294]
  efficient graph analysis using modern machine learning is receiving a growing level of attention. Deep learning approaches often operate over the entire adjacency matrix. It is desirable to identify efficient measures to reduce both run-time and memory requirements.
  arXiv  Detail & Related papers  (2020-10-23T19:47:42Z)
• Understanding Coarsening for Embedding Large-Scale Graphs [3.6739949215165164]
  Proper analysis of graphs with Machine Learning (ML) algorithms has the potential to yield far-reaching insights into many areas of research and industry. The irregular structure of graph data constitutes an obstacle for running ML tasks on graphs. We analyze the impact of the coarsening quality on the embedding performance both in terms of speed and accuracy.
  arXiv  Detail & Related papers  (2020-09-10T15:06:33Z)
• SIGN: Scalable Inception Graph Neural Networks [4.5158585619109495]
  We propose a new, efficient and scalable graph deep learning architecture that sidesteps the need for graph sampling. Our architecture allows using different local graph operators to best suit the task at hand. We obtain state-of-the-art results on ogbn-papers100M, the largest public graph dataset, with over 110 million nodes and 1.5 billion edges.
  arXiv  Detail & Related papers  (2020-04-23T14:46:10Z)
• Geometrically Principled Connections in Graph Neural Networks [66.51286736506658]
  We argue geometry should remain the primary driving force behind innovation in the emerging field of geometric deep learning. We relate graph neural networks to widely successful computer graphics and data approximation models: radial basis functions (RBFs) We introduce affine skip connections, a novel building block formed by combining a fully connected layer with any graph convolution operator.
  arXiv  Detail & Related papers  (2020-04-06T13:25:46Z)

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.