用指令序列表示图结构，让深度学习模型更高效处理图数据。
Representation of the structure of graphs by sequences of instructions
- 用可逆的简单指令逐步构建邻接矩阵
- 实验显示分类准确率提升且计算更快
- 适合想用文本模型处理图数据的研究者
图的表示通常基于邻接矩阵，这是大多数代数和计算图处理方法的基础。深度学习语言模型在文本处理方面表现出强大能力，但现有图表示方式难以适配这些模型。本文提出一种新方法：将图的邻接矩阵转化为一串简单指令序列，按步骤生成矩阵，该转换是可逆的——给定图可生成序列，反之亦然。该表示紧凑，并保留图的局部结构特征，有望提升深度学习模型对图的处理效率。初步计算实验表明，采用该表示后分类性能提高，计算时间缩短。
原文摘要 · Abstract (English)
The representation of graphs is commonly based on the adjacency matrix concept. This formulation is the foundation of most algebraic and computational approaches to graph processing. The advent of deep learning language models offers a wide range of powerful computational models that are specialized in the processing of text. However, current procedures to represent graphs are not amenable to processing by these models. In this work, a new method to represent graphs is proposed. It represents the adjacency matrix of a graph by a string of simple instructions. The instructions build the adjacency matrix step by step. The transformation is reversible, i.e., given a graph the string can be produced and vice versa. The proposed representation is compact, and it maintains the local structural patterns of the graph. Therefore, it is envisaged that it could be useful to boost the processing of graphs by deep learning models. A tentative computational experiment is reported, demonstrating improved classification performance and faster computation times with the proposed representation.
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