为无特征图网络设计通用属性编码器，提升节点表示能力
PropEnc: A Property Encoder for Graph Neural Networks
- 用直方图+逆索引编码融合生成节点嵌入
- 在多个社交网络上实现高精度图分类性能
- 适合缺乏节点特征的现实图数据场景
图机器学习依赖节点特征，但许多真实系统（如社交、生物网络）因隐私或数据不全而缺乏节点特征。现有结构和位置编码受限于编码属性的最大值（如最高节点度），难以适用于无标度网络或大范围非类别属性。本文提出PropEnc，一种新型通用编码器，可从任意图度量生成丰富节点嵌入。通过直方图构建与逆索引编码结合，支持低维表示与多种输入类型，有效缓解稀疏性并提升计算效率。同时能复现一热编码或高精度近似索引，适用于广泛图应用。在多个无节点特征的社交网络上进行图分类实验，结果表明PropEnc能高效利用各类图度量构造节点特征。
原文摘要 · Abstract (English)
Graph machine learning, particularly using graph neural networks, heavily relies on node features. However, many real-world systems, such as social and biological networks, lack node features due to privacy concerns, incomplete data, or collection limitations. Structural and positional encoding are commonly used to address this but are constrained by the maximum values of the encoded properties, such as the highest node degree. This limitation makes them impractical for scale-free networks and applications involving large or non-categorical properties. This paper introduces PropEnc, a novel and versatile encoder to generate expressive node embedding from any graph metric. By combining histogram construction with reversed index encoding, PropEnc offers a flexible solution that supports low-dimensional representations and diverse input types, effectively mitigating sparsity issues while improving computational efficiency. Additionally, it replicates one-hot encoding or approximates indices with high accuracy, making it adaptable to a wide range of graph applications. We validate PropEnc through extensive experiments on graph classification task across several social networks lacking node features. The empirical results demonstrate that PropEnc offers an efficient mechanism for constructing node features from various graph metrics.
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