用局部内在维数识别动态图嵌入中表现差的节点
Local Intrinsic Dimensionality for Dynamic Graph Embeddings
- 将静态图的NC-LID度量拓展至动态图，捕捉节点嵌入质量
- 实验证明NC-LID高的节点其嵌入难以保留时间结构
- 为构建感知局部维数的动态图嵌入方法提供基础
局部内在维数（LID）在数据挖掘与机器学习领域具有重要理论意义和实际应用价值。近期研究表明，针对图结构定义的LID度量可提升基于随机游走的图表示学习方法。本文探讨了专为静态图设计的NC-LID度量在动态网络中的适用性。以基于随机游走的代表性动态图嵌入方法dynnode2vec为例，我们分析了NC-LID与10个真实动态网络嵌入内在质量之间的相关性。结果表明，NC-LID可有效指示那些嵌入向量未能良好保持时间图结构的节点。这一实证发现为构建感知局部内在维数的动态图嵌入方法迈出了第一步。
原文摘要 · Abstract (English)
The notion of local intrinsic dimensionality (LID) has important theoretical implications and practical applications in the fields of data mining and machine learning. Recent research efforts indicate that LID measures defined for graphs can improve graph representational learning methods based on random walks. In this paper, we discuss how NC-LID, a LID measure designed for static graphs, can be adapted for dynamic networks. Focusing on dynnode2vec as the most representative dynamic graph embedding method based on random walks, we examine correlations between NC-LID and the intrinsic quality of 10 real-world dynamic network embeddings. The obtained results show that NC-LID can be used as a good indicator of nodes whose embedding vectors do not tend to preserve temporal graph structure well. Thus, our empirical findings constitute the first step towards LID-aware dynamic graph embedding methods.
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