HUGE: Huge Unsupervised Graph Embeddings with TPUs

• URL: http://arxiv.org/abs/2307.14490v1
• Date: Wed, 26 Jul 2023 20:29:15 GMT
• Title: HUGE: Huge Unsupervised Graph Embeddings with TPUs
• Authors: Brandon Mayer, Anton Tsitsulin, Hendrik Fichtenberger, Jonathan Halcrow, Bryan Perozzi
• Abstract summary: Graph Embedding is a process of creating a continuous representation of nodes in a graph. A high-performance graph embedding architecture leveraging amounts of high-bandwidth memory is presented. We verify the embedding space quality on real and synthetic large-scale datasets.
• Score: 6.108914274067702
• License: http://creativecommons.org/publicdomain/zero/1.0/
• Abstract: Graphs are a representation of structured data that captures the relationships between sets of objects. With the ubiquity of available network data, there is increasing industrial and academic need to quickly analyze graphs with billions of nodes and trillions of edges. A common first step for network understanding is Graph Embedding, the process of creating a continuous representation of nodes in a graph. A continuous representation is often more amenable, especially at scale, for solving downstream machine learning tasks such as classification, link prediction, and clustering. A high-performance graph embedding architecture leveraging Tensor Processing Units (TPUs) with configurable amounts of high-bandwidth memory is presented that simplifies the graph embedding problem and can scale to graphs with billions of nodes and trillions of edges. We verify the embedding space quality on real and synthetic large-scale datasets.

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)
• GLISP: A Scalable GNN Learning System by Exploiting Inherent Structural Properties of Graphs [5.410321469222541]
  We propose GLISP, a sampling based GNN learning system for industrial scale graphs. GLISP consists of three core components: graph partitioner, graph sampling service and graph inference engine. Experiments show that GLISP achieves up to $6.53times$ and $70.77times$ speedups over existing GNN systems for training and inference tasks.
  arXiv  Detail & Related papers  (2024-01-06T02:59:24Z)
• Graph Transformers for Large Graphs [57.19338459218758]
  This work advances representation learning on single large-scale graphs with a focus on identifying model characteristics and critical design constraints. A key innovation of this work lies in the creation of a fast neighborhood sampling technique coupled with a local attention mechanism. We report a 3x speedup and 16.8% performance gain on ogbn-products and snap-patents, while we also scale LargeGT on ogbn-100M with a 5.9% performance improvement.
  arXiv  Detail & Related papers  (2023-12-18T11:19:23Z)
• NodeFormer: A Scalable Graph Structure Learning Transformer for Node Classification [70.51126383984555]
  We introduce a novel all-pair message passing scheme for efficiently propagating node signals between arbitrary nodes. The efficient computation is enabled by a kernerlized Gumbel-Softmax operator. Experiments demonstrate the promising efficacy of the method in various tasks including node classification on graphs.
  arXiv  Detail & Related papers  (2023-06-14T09:21:15Z)
• SynGraphy: Succinct Summarisation of Large Networks via Small Synthetic Representative Graphs [4.550112751061436]
  We describe SynGraphy, a method for visually summarising the structure of large network datasets. It works by drawing smaller graphs generated to have similar structural properties to the input graphs.
  arXiv  Detail & Related papers  (2023-02-15T16:00:15Z)
• 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)
• 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)
• A Robust and Generalized Framework for Adversarial Graph Embedding [73.37228022428663]
  We propose a robust framework for adversarial graph embedding, named AGE. AGE generates the fake neighbor nodes as the enhanced negative samples from the implicit distribution. Based on this framework, we propose three models to handle three types of graph data.
  arXiv  Detail & Related papers  (2021-05-22T07:05:48Z)
• 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)
• MathNet: Haar-Like Wavelet Multiresolution-Analysis for Graph Representation and Learning [31.42901131602713]
  We propose a framework for graph neural networks with multiresolution Haar-like wavelets, or MathNet, with interrelated convolution and pooling strategies. The proposed MathNet outperforms various existing GNN models, especially on big data sets.
  arXiv  Detail & Related papers  (2020-07-22T05:00:59Z)
• GCC: Graph Contrastive Coding for Graph Neural Network Pre-Training [62.73470368851127]
  Graph representation learning has emerged as a powerful technique for addressing real-world problems. We design Graph Contrastive Coding -- a self-supervised graph neural network pre-training framework. We conduct experiments on three graph learning tasks and ten graph datasets.
  arXiv  Detail & Related papers  (2020-06-17T16:18:35Z)

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.