结束:图形压缩和嵌入的增强去密度化

Tanvir Hossain, Esra Akbas, Muhammad Ifte Khairul Islam
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引用次数: 0

摘要

图表示学习是将机器学习方法应用于大规模网络的关键。近年来,几种嵌入方法已经显示出有希望的结果。然而,在海量图上,直接应用现有的嵌入方法可能会耗费时间和空间。本文提出了一种新的基于脱密的图形压缩方法,称为基于程度压缩的增强脱密。我们系统的主要目标是确保对大型图进行适当的压缩,从而有力地支持它们的表示学习。为此,我们首先压缩低度节点并对其进行致密化,以减少高度节点的负载。然后,我们嵌入压缩图代替原始图,以减少表示学习成本。我们的方法是一种通用的元策略,通过应用最先进的图嵌入方法(Node2vec、DeepWalk、RiWalk和xNetMf),在原始图上实现时间和空间效率。在大规模真实图上的综合实验验证了我们的方法的可行性,该方法在单标签和多标签节点分类任务上表现出良好的性能,同时又不失准确性。
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EnD: Enhanced Dedensification for Graph Compressing and Embedding
Graph representation learning is essential in applying machine learning methods on large-scale networks. Several embedding approaches have shown promising outcomes in recent years. Nonetheless, on massive graphs, it may be time-consuming and space inefficient for direct applications of existing embedding methods. This paper presents a novel graph compression approach based on dedensification called Enhanced Dedensification with degree-based compression (EnD). The principal goal of our system is to assure decent compression of large graphs that eloquently favor their representation learning. For this purpose, we first compress the low-degree nodes and dedensify them to reduce the high-degree nodes' loads. Then, we embed the compressed graph instead of the original graph to decrease the representation learning cost. Our approach is a general meta-strategy that attains time and space efficiency over the original graph by applying the state-of-the-art graph embedding methods: Node2vec, DeepWalk, RiWalk, and xNetMf. Comprehensive ex-periments on large-scale real-world graphs validate the viability of our method, which shows sound performance on single and multi-label node classification tasks without losing accuracy.
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