提出可灵活捕捉多种结构模式的高效节点嵌入方法
ffstruc2vec: Flat, Flexible and Scalable Learning of Node Representations from Structural Identities
- 采用扁平高效架构，灵活学习各类图结构特征
- 在多类下游任务中显著优于现有方法
- 支持结构模式对任务结果的影响量化解释
节点嵌入旨在生成保留节点特定属性的低维向量表示。当前主要挑战在于如何设计可扩展的方法，以适应下游任务所需的各类结构模式。尽管多数现有方法聚焦于保持节点邻近性，但能真正保留结构特性的方法往往缺乏灵活性，难以满足多样化任务需求。本文提出 ffstruc2vec，一种可扩展的深度学习框架，用于学习保留结构身份的节点嵌入。其扁平高效的架构使模型能灵活捕捉多种结构模式，具备广泛适用性。实验表明，该框架在各类无监督与有监督任务中均显著优于现有方法。此外，ffstruc2vec 可通过量化各结构模式对任务结果的影响，实现可解释性分析。据我们所知，目前尚无其他框架同时具备如此高的灵活性、可扩展性与结构可解释性，凸显其独特优势。
原文摘要 · Abstract (English)
Node embedding refers to techniques that generate low-dimensional vector representations of nodes in a graph while preserving specific properties of the nodes. A key challenge in the field is developing scalable methods that can preserve structural properties suitable for the required types of structural patterns of a given downstream application task. While most existing methods focus on preserving node proximity, those that do preserve structural properties often lack the flexibility to preserve various types of structural patterns required by downstream application tasks. This paper introduces ffstruc2vec, a scalable deep-learning framework for learning node embedding vectors that preserve structural identities. Its flat, efficient architecture allows high flexibility in capturing diverse types of structural patterns, enabling broad adaptability to various downstream application tasks. The proposed framework significantly outperforms existing approaches across diverse unsupervised and supervised tasks in practical applications. Moreover, ffstruc2vec enables explainability by quantifying how individual structural patterns influence task outcomes, providing actionable interpretation. To our knowledge, no existing framework combines this level of flexibility, scalability, and structural interpretability, underscoring its unique capabilities.
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