H²TNE：双曲空间中的时序异质信息网络嵌入
$\text{H}^2\text{TNE}$: Temporal Heterogeneous Information Network Embedding in Hyperbolic Spaces
AI总结：
针对真实时序异质网络的层次与幂律结构，提出 H²TNE 模型，通过双重约束随机游走和双曲距离学习嵌入，并在时序链接预测与节点分类上优于 SOTA。
摘要：
时序异质信息网络（temporal HIN）嵌入旨在将不同时间戳的多种类型节点表示到低维空间中，同时保留结构和语义信息，在各类现实任务中具有重要意义。研究人员已在欧几里得空间中的时序 HIN 嵌入方面付出大量努力，并取得了一些可观成果。然而，始终存在一个根本性冲突：许多真实网络表现出层次属性和幂律分布，并且与欧几里得空间不等距。近年来，双曲空间中的表示学习已被证明对具有层次和幂律结构的数据有效。受这一特性启发，我们针对时序 HIN 提出了一种双曲异质时序网络嵌入（H²TNE）模型。具体而言，我们利用时间和异质双重约束的随机游走策略来捕获结构与语义信息，随后在邻近性度量中利用双曲距离来计算嵌入。实验结果表明，与 SOTA 模型相比，我们的方法在时序链接预测和节点分类任务上具有优越性能。
英文摘要：
Temporal heterogeneous information network (temporal HIN) embedding, aiming to represent various types of nodes of different timestamps into low dimensional spaces while preserving structural and semantic information, is of vital importance in diverse real-life tasks. Researchers have made great efforts on temporal HIN embedding in Euclidean spaces and got some considerable achievements. However, there is always a fundamental conflict that many real-world networks show hierarchical property and power-law distribution, and are not isometric of Euclidean spaces. Recently, representation learning in hyperbolic spaces has been proved to be valid for data with hierarchical and power-law structure. Inspired by this character, we propose a hyperbolic heterogeneous temporal network embedding ($\text{H}^2\text{TNE}$) model for temporal HINs. Specifically, we leverage a temporally and heterogeneously double-constrained random walk strategy to capture the structural and semantic information, and then calculate the embedding by exploiting hyperbolic distance in proximity measurement. Experimental results show that our method has superior performance on temporal link prediction and node classification compared with SOTA models.
机构、总结、中文摘要均由AI分析而来，可能存在错误，请以论文正文为准。
