Biquaternionic Space with Complex-valued Attention for Temporal Knowledge Graph Completion

• URL: http://arxiv.org/abs/2609.14279v1
• Date: Sun, 13 Sep 2026 04:32:12 GMT
• Title: Biquaternionic Space with Complex-valued Attention for Temporal Knowledge Graph Completion
• Abstract summary: We propose a TKGE model that combines circular and hyperbolic rotations within a unified biquaternionic framework.<n>A complex-valued attention mechanism adaptively fuses time-conditioned and relation-conditioned entity representations, allowing them to vary with temporal and relational context.
• Score: 0.8415095580471554
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: Temporal knowledge graph embedding (TKGE) models infer missing facts in knowledge graphs that evolve over time. Many existing models use a single geometric space, which can limit their ability to represent diverse relational patterns, or treat entity representations as static. We propose Biquaternionic Space with Complex-valued Attention (BSCA), a TKGE model that combines circular and hyperbolic rotations within a unified biquaternionic framework. A complex-valued attention mechanism adaptively fuses time-conditioned and relation-conditioned entity representations, allowing them to vary with temporal and relational context. Experiments on five benchmark datasets show competitive performance across datasets, with the largest improvement on GDELT: BSCA achieves an MRR of 52.1\%, compared with 38.1\% for the strongest baseline in our comparison.

Related papers

• T-Graphormer: Using Transformers for Spatiotemporal Forecasting [2.855856661274715]
  T-Graphormer reduces root mean squared error (RMSE) and mean absolute percentage error (MAPE) by up to 20% and 10%.<n>We show the effectiveness of T-Graphormer on real-world traffic prediction benchmark datasets.
  arXiv  Detail & Related papers  (2025-01-22T23:32:29Z)
• Fully Hyperbolic Rotation for Knowledge Graph Embedding [12.69417276887153]
  We propose a novel fully hyperbolic model designed for knowledge graph embedding. Our model considers each relation in knowledge graphs as a Lorentz rotation from the head entity to the tail entity. Our model achieves competitive results with fewer parameters.
  arXiv  Detail & Related papers  (2024-11-06T02:41:26Z)
• Temporal Knowledge Graph Completion with Time-sensitive Relations in Hypercomplex Space [20.235189945656927]
  Temporal knowledge graph completion (TKGC) aims to fill in missing facts within a given temporal knowledge graph at a specific time. This paper advances beyond conventional approaches by introducing more expressive quaternion representations for TKGC within hypercomplex space.
  arXiv  Detail & Related papers  (2024-03-02T16:50:48Z)
• Deep Temporal Graph Clustering [77.02070768950145]
  We propose a general framework for deep Temporal Graph Clustering (GC) GC introduces deep clustering techniques to suit the interaction sequence-based batch-processing pattern of temporal graphs. Our framework can effectively improve the performance of existing temporal graph learning methods.
  arXiv  Detail & Related papers  (2023-05-18T06:17:50Z)
• ChiroDiff: Modelling chirographic data with Diffusion Models [132.5223191478268]
  We introduce a powerful model-class namely "Denoising Diffusion Probabilistic Models" or DDPMs for chirographic data. Our model named "ChiroDiff", being non-autoregressive, learns to capture holistic concepts and therefore remains resilient to higher temporal sampling rate.
  arXiv  Detail & Related papers  (2023-04-07T15:17:48Z)
• Time-aware Hyperbolic Graph Attention Network for Session-based Recommendation [58.748215444851226]
  Session-based Recommendation (SBR) is to predict users' next interested items based on their previous browsing sessions. We propose Time-aware Hyperbolic Graph Attention Network (TA-HGAT) to build a session-based recommendation model considering temporal information.
  arXiv  Detail & Related papers  (2023-01-10T04:16:09Z)
• Adaptively-weighted Integral Space for Fast Multiview Clustering [54.177846260063966]
  We propose an Adaptively-weighted Integral Space for Fast Multiview Clustering (AIMC) with nearly linear complexity. Specifically, view generation models are designed to reconstruct the view observations from the latent integral space. Experiments conducted on several realworld datasets confirm the superiority of the proposed AIMC method.
  arXiv  Detail & Related papers  (2022-08-25T05:47:39Z)
• A Simple But Powerful Graph Encoder for Temporal Knowledge Graph Completion [13.047205680129094]
  We propose a simple but powerful graph encoder TARGCN for temporal knowledge graphs (TKGs) Our model can achieve a more than 42% relative improvement on GDELT dataset compared with the state-of-the-art model. It outperforms the strongest baseline on ICEWS05-15 dataset with around 18.5% fewer parameters.
  arXiv  Detail & Related papers  (2021-12-14T23:30:42Z)
• BiQUE: Biquaternionic Embeddings of Knowledge Graphs [9.107095800991333]
  Existing knowledge graph embeddings (KGEs) compactly encode multi-relational knowledge graphs (KGs) It is crucial for KGE models to unify multiple geometric transformations so as to fully cover the multifarious relations in KGs. We propose BiQUE, a novel model that employs biquaternions to integrate multiple geometric transformations.
  arXiv  Detail & Related papers  (2021-09-29T13:05:32Z)
• Spatial-spectral Hyperspectral Image Classification via Multiple Random Anchor Graphs Ensemble Learning [88.60285937702304]
  This paper proposes a novel spatial-spectral HSI classification method via multiple random anchor graphs ensemble learning (RAGE) Firstly, the local binary pattern is adopted to extract the more descriptive features on each selected band, which preserves local structures and subtle changes of a region. Secondly, the adaptive neighbors assignment is introduced in the construction of anchor graph, to reduce the computational complexity.
  arXiv  Detail & Related papers  (2021-03-25T09:31:41Z)
• GTEA: Inductive Representation Learning on Temporal Interaction Graphs via Temporal Edge Aggregation [11.526912398475513]
  We propose the Graph Temporal Edge Aggregation framework for inductive learning on Temporal Interaction Graphs (TIGs) By aggregating features of neighboring nodes and the corresponding edge embeddings, GTEA jointly learns both topological and temporal dependencies of a TIG. In addition, a sparsity-inducing self-attention scheme is incorporated for neighbor aggregation, which highlights more important neighbors and suppresses trivial noises for GTEA.
  arXiv  Detail & Related papers  (2020-09-11T07:52:05Z)
• Disentangling and Unifying Graph Convolutions for Skeleton-Based Action Recognition [79.33539539956186]
  We propose a simple method to disentangle multi-scale graph convolutions and a unified spatial-temporal graph convolutional operator named G3D. By coupling these proposals, we develop a powerful feature extractor named MS-G3D based on which our model outperforms previous state-of-the-art methods on three large-scale datasets.
  arXiv  Detail & Related papers  (2020-03-31T11:28:25Z)

This list is automatically generated from the titles and abstracts of the papers in this site.

This site does not guarantee the quality of this site (including all information) and is not responsible for any consequences.