A Property Encoder for Graph Neural Networks

Anwar Said, Xenofon Koutsoukos
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Abstract

Graph machine learning, particularly using graph neural networks, fundamentally relies on node features. Nevertheless, numerous real-world systems, such as social and biological networks, often lack node features due to various reasons, including privacy concerns, incomplete or missing data, and limitations in data collection. In such scenarios, researchers typically resort to methods like structural and positional encoding to construct node features. However, the length of such features is contingent on the maximum value within the property being encoded, for example, the highest node degree, which can be exceedingly large in applications like scale-free networks. Furthermore, these encoding schemes are limited to categorical data and might not be able to encode metrics returning other type of values. In this paper, we introduce a novel, universally applicable encoder, termed PropEnc, which constructs expressive node embedding from any given graph metric. PropEnc leverages histogram construction combined with reverse index encoding, offering a flexible method for node features initialization. It supports flexible encoding in terms of both dimensionality and type of input, demonstrating its effectiveness across diverse applications. PropEnc allows encoding metrics in low-dimensional space which effectively avoids the issue of sparsity and enhances the efficiency of the models. We show that \emph{PropEnc} can construct node features that either exactly replicate one-hot encoding or closely approximate indices under various settings. Our extensive evaluations in graph classification setting across multiple social networks that lack node features support our hypothesis. The empirical results conclusively demonstrate that PropEnc is both an efficient and effective mechanism for constructing node features from diverse set of graph metrics.
图神经网络的属性编码器
图机器学习,尤其是使用图神经网络的机器学习,从根本上依赖于节点特征。然而,现实世界中的许多系统,如社会和生物网络,往往由于各种原因而缺乏节点特征,包括隐私问题、数据不完整或缺失以及数据收集的限制。在这种情况下,研究人员通常采用结构编码和位置编码等方法来构建节点特征。然而,这些特征的长度取决于被编码属性的最大值,例如最高节点度,而在无标度网络等应用中,最高节点度可能会非常大。此外,这些编码方案仅限于分类数据,可能无法对返回其他类型值的度量进行编码。在本文中,我们介绍了一种新的、普遍适用的编码器,称为 PropEnc,它可以从任何给定的图度量中构建具有表达力的节点嵌入。PropEnc 利用直方图构造与反向索引编码相结合,为节点特征初始化提供了一种灵活的方法。它支持对输入的维度和类型进行灵活编码,在各种应用中都证明了它的有效性。PropEnc 允许在低维空间中对指标进行编码,从而有效避免了稀疏性问题,并提高了模型的效率。我们的研究表明,PropEnc 可以在各种设置下构建节点特征,这些特征可以完全复制单次热编码,也可以近似于指数。我们在缺乏节点特征的多个社交网络的图分类设置中进行的广泛评估支持了我们的假设。实证结果最终证明,PropEnc 是一种高效且有效的机制,可以从不同的图度量集合中构建节点特征。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
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