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首页> 外文期刊>Neural Networks and Learning Systems, IEEE Transactions on >GrAMME: Semisupervised Learning Using Multilayered Graph Attention Models
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GrAMME: Semisupervised Learning Using Multilayered Graph Attention Models

机译：格拉姆：使用多层图注意模型的半熟学习

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摘要

Modern data analysis pipelines are becoming increasingly complex due to the presence of multiview information sources. While graphs are effective in modeling complex relationships, in many scenarios, a single graph is rarely sufficient to succinctly represent all interactions, and hence, multilayered graphs have become popular. Though this leads to richer representations, extending solutions from the single-graph case is not straightforward. Consequently, there is a strong need for novel solutions to solve classical problems, such as node classification, in the multilayered case. In this article, we consider the problem of semisupervised learning with multilayered graphs. Though deep network embeddings, e.g., DeepWalk, are widely adopted for community discovery, we argue that feature learning with random node attributes, using graph neural networks, can be more effective. To this end, we propose to use attention models for effective feature learning and develop two novel architectures, GrAMME-SG and GrAMME-Fusion, that exploit the interlayer dependences for building multilayered graph embeddings. Using empirical studies on several benchmark data sets, we evaluate the proposed approaches and demonstrate significant performance improvements in comparison with the state-of-the-art network embedding strategies. The results also show that using simple random features is an effective choice, even in cases where explicit node attributes are not available.
机译：由于存在多视图信息来源，现代数据分析流水线正在变得越来越复杂。虽然图表在模拟复杂关系中有效，但在许多场景中，单个图形很少足以为简洁地代表所有交互，因此，多层图已变得流行。虽然这导致更丰富的表示，但从单图盒中扩展了解决方案并不简单。因此，在多层壳体中，有强烈需要解决诸如节点分类的经典问题。在本文中，我们考虑了多层图的半体育学习问题。虽然深网络嵌入式，例如Deptwalk被广泛用于社区发现，但我们认为具有随机节点属性的功能学习，使用图形神经网络，可以更有效。为此，我们建议使用注意模型进行有效的特征学习，并开发两种新颖的架构，格拉姆-SG和语言融合，这利用了构建多层图形嵌入的层间依赖。使用对多个基准数据集的实证研究，我们评估了所提出的方法，并表现出与最先进的网络嵌入策略相比的显着性能改进。结果还表明，使用简单的随机功能是一种有效的选择，即使在未使用显式节点属性的情况下也是有效的选择。

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