Abstract

Deep learning has recently been shown to be effective in uncovering hidden patterns in non-Euclidean space, where data is represented as graphs with complex object relationships and interdependencies. Because of the implicit data dependence in the big graphs with millions of nodes and billions of edges, it is hard for industrial communities to exploit these methods to address real-world challenges at scale. The skewness property of big graphs, distributed file system performance penalty on small k-hop neighborhood subgraphs, and varying size of subgraph makes Graph Neural Networks (GNNs) training further challenging in a distributed environment using parameter servers. To address such issues, we propose a scalable, layered, fault-tolerance, and in-memory distributed computing-based graph neural network framework called Graph Distributed Learning Library (GDLL). The base layer utilizes an optimized distributed file system and a scalable graph data store to reduce the performance penalty. The second layer provides distributed graph processing using in-memory graph programming models while optimizing and hiding the underlying complexity of information complete subgraph computation. In the third layer, GNN modules are deployed on top of the first two layers for efficient distributed training using parameter servers. Finally, we evaluate and compare GDLL with the state-of-the-art solutions and outperform it significantly in terms of efficiency while maintaining similar GNN convergence.

Highlights

  • G RAPH topologies may naturally represent real-world data in a variety of applications

  • Existing frameworks focus more on the training of graph learning models but overlook system integrity and generalizability. To address such issues and to fill the research gap, in this paper, we present Graph Distributed Learning Library (GDLL), a scalable, layered, fault-tolerance, in-memory, and shared-nothing architecture-based framework for distributed Graph Neural Networkss (GNNs) training

  • The proposed framework is composed of three layers, i.e., Graph Data Layer (GDL), Graph Optimization Layer (GOL), and Graph Learning Layer (GLL)

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Summary

INTRODUCTION

G RAPH topologies may naturally represent real-world data in a variety of applications. AliGraph implements distributed inmemory graph storage engine, which requires standalone deployment before training a GNN model In the latter class, NeuGraph [12] is based on the Scatter Apply Gather [13] graph processing model. Existing frameworks focus more on the training of graph learning models but overlook system integrity and generalizability To address such issues and to fill the research gap, in this paper, we present Graph Distributed Learning Library (GDLL), a scalable, layered, fault-tolerance, in-memory, and shared-nothing architecture-based framework for distributed GNNs training. The second layer (section IV-B) is called GOL, which provides distributed graph processing on top of an in-memory MapReduce framework (Apache Spark [15]) while optimizing and hiding the underlying complexity of advanced message passing, k-hop-based subgraph computation, and graph sampling techniques. GNNs. We implement the proposed GDLL framework and conduct extensive experiments to validate our claims

RELATED WORK
NOTATIONS
GNNS AS MESSAGE PASSING
C Current State
K-HOP NEIGHBORHOOD
PROPOSED GDLL FRAMEWORK
GRAPH DATA LAYER
K-Hop based subgraph computation
GDLL LIBRARY AND SCENARIOS
GDLL RESULTS AND EVALUATION
DATASET
DISTRIBUTED GNN TRAINING
Methods
CONCLUSION
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