mlsanityData to 7 Oct 2026

H2TNE: Temporal Heterogeneous Information Network Embedding in Hyperbolic Spaces

arXiv 2304.06970 ↗First posted 14 Apr 2023cs.SI cs.AIcited by 3

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Abstract (arXiv)

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 (H2TNE) 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.

Key facts. Cited by 3 papers in the map (data through 7 Oct 2026). Builds on 25 of its 42 references that are in the map. First posted on arXiv 14 Apr 2023.

Builds on

25 of 42 references.

References · 25

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13 more
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Built on by

Cited by year (2026 to 7 Oct)

Citing papers per year: 2023: 1; 2024: 1; 2025: 0; 2026: 1.

3 papers.

  1. 2023Hyperbolic Representation Learning: Revisiting and Advancingcited by 21
  2. 2024Disentangled Hyperbolic Representation Learning for Heterogeneous Graphscited by 0
  3. 2026Dynamic Heterogeneous Graph Representation Learning: A Surveycited by 0