$\text{H}^2\text{TNE}$: Temporal Heterogeneous Information Network Embedding in Hyperbolic Spaces

• URL: http://arxiv.org/abs/2304.06970v3
• Date: Fri, 14 Jun 2024 18:43:40 GMT
• Title: $\text{H}^2\text{TNE}$: Temporal Heterogeneous Information Network Embedding in Hyperbolic Spaces
• Authors: Qijie Bai, Jiawen Guo, Haiwei Zhang, Changli Nie, Lin Zhang, Xiaojie Yuan,
• Abstract summary: We propose a hyperbolic heterogeneous temporal network embedding model for temporal HINs. Specifically, we leverage a temporally and heterogeneously double-constrained random walk strategy to capture the structural and semantic information. Experimental results show that our method has superior performance on temporal link prediction and node classification compared with SOTA models.
• Score: 16.31067633778912
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: 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.

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