# CHE-TKG: Collaborative Historical Evidence and Evolutionary Dynamics Learning for Temporal Knowledge Graph Reasoning

arXiv: https://arxiv.org/abs/2605.04652 · HTML: https://mlsanity.com/paper/2605.04652/

## Key facts

- No citing papers in the map yet (data through 14 Oct 2026).
- Builds on 26 of its 55 references that are in the map.
- First posted on arXiv 6 May 2026.

## Builds on (most cited first, 26 of 26)

- [GLU Variants Improve Transformer](https://mlsanity.com/paper/2002.05202/) (2020)
- [Inductive Representation Learning on Temporal Graphs](https://mlsanity.com/paper/2002.07962/) (2020)
- [Gradient Starvation: A Learning Proclivity in Neural Networks](https://mlsanity.com/paper/2011.09468/) (2020)
- [Tensor Decompositions for temporal knowledge base completion](https://mlsanity.com/paper/2004.04926/) (2020)
- [Temporal Knowledge Graph Reasoning Based on Evolutional Representation Learning](https://mlsanity.com/paper/2104.10353/) (2021)
- [Retrieval-Augmented Generation with Knowledge Graphs for Customer Service Question Answering](https://mlsanity.com/paper/2404.17723/) (2024)
- [Temporal Knowledge Graph Forecasting with Neural ODE](https://mlsanity.com/paper/2101.05151/) (2021)
- [TLogic: Temporal Logical Rules for Explainable Link Forecasting on Temporal Knowledge Graphs](https://mlsanity.com/paper/2112.08025/) (2021)
- [Which Shortcut Cues Will DNNs Choose? A Study from the Parameter-Space Perspective](https://mlsanity.com/paper/2110.03095/) (2021)
- [Large Language Models-guided Dynamic Adaptation for Temporal Knowledge Graph Reasoning](https://mlsanity.com/paper/2405.14170/) (2024)
- [Temporal Knowledge Graph Forecasting Without Knowledge Using In-Context Learning](https://mlsanity.com/paper/2305.10613/) (2023)
- [Temporal Knowledge Graph Completion using Box Embeddings](https://mlsanity.com/paper/2109.08970/) (2021)
- [Complex Evolutional Pattern Learning for Temporal Knowledge Graph Reasoning](https://mlsanity.com/paper/2203.07782/) (2022)
- [GenTKG: Generative Forecasting on Temporal Knowledge Graph with Large Language Models](https://mlsanity.com/paper/2310.07793/) (2023)
- [Chain-of-History Reasoning for Temporal Knowledge Graph Forecasting](https://mlsanity.com/paper/2402.14382/) (2024)
- [Enhancing Complex Question Answering over Knowledge Graphs through Evidence Pattern Retrieval](https://mlsanity.com/paper/2402.02175/) (2024)
- [HiSMatch: Historical Structure Matching based Temporal Knowledge Graph Reasoning](https://mlsanity.com/paper/2210.09708/) (2022)
- [Adaptive Path-Memory Network for Temporal Knowledge Graph Reasoning](https://mlsanity.com/paper/2304.12604/) (2023)
- [Local-Global History-aware Contrastive Learning for Temporal Knowledge Graph Reasoning](https://mlsanity.com/paper/2312.01601/) (2023)
- [Transformer-based Reasoning for Learning Evolutionary Chain of Events on Temporal Knowledge Graph](https://mlsanity.com/paper/2405.00352/) (2024)
- [GTRL: An Entity Group-Aware Temporal Knowledge Graph Representation Learning Method](https://mlsanity.com/paper/2302.11091/) (2023)
- [Simple but Effective Compound Geometric Operations for Temporal Knowledge Graph Completion](https://mlsanity.com/paper/2408.06603/) (2024)
- [DECRL: A Deep Evolutionary Clustering Jointed Temporal Knowledge Graph Representation Learning Approach](https://mlsanity.com/paper/2410.22631/) (2024)
- [Arbitrary Time Information Modeling via Polynomial Approximation for Temporal Knowledge Graph Embedding](https://mlsanity.com/paper/2405.00358/) (2024)
- [Hawkes based Representation Learning for Reasoning over Scale-free Community-structured Temporal Knowledge Graphs](https://mlsanity.com/paper/2501.01974/) (2024)
- [G2S: A General-to-Specific Learning Framework for Temporal Knowledge Graph Forecasting with Large Language Models](https://mlsanity.com/paper/2506.00445/) (2025)

## Abstract (arXiv)

Temporal knowledge graph (TKG) reasoning aims to predict future events from historical facts. A key challenge lies in jointly capturing two sources of predictive information in TKGs: historical evidence and evolutionary dynamics. However, existing methods typically focus on only one of these sources, which limits the ability to fully exploit the complementary predictive signals in TKGs. To address this, we propose CHE-TKG, a novel collaborative dual-view learning framework for TKG reasoning. CHE-TKG explicitly separates and jointly models historical evidence and evolutionary dynamics, aiming to learn and exploit their complementary predictive signals. Specifically, CHE-TKG constructs a historical evidence graph to capture long-term structural regularities and stable relational constraints, alongside an evolutionary dynamics graph to model temporal transitions and recent changes, with dedicated encoders for each view. We further employ relation decomposition and a contrastive alignment objective to better capture the predictive signals across the two views. Extensive experiments demonstrate that CHE-TKG achieves state-of-the-art performance on multiple benchmarks.