# Recurrent Event Network: Autoregressive Structure Inference over Temporal Knowledge Graphs

arXiv: https://arxiv.org/abs/1904.05530 · HTML: https://mlsanity.com/paper/1904.05530/

## Key facts

- Cited by 12 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 11 Apr 2019.

## Built on by (most cited first, 12 of 12)

- [Temporal Knowledge Graph Reasoning Based on Evolutional Representation Learning](https://mlsanity.com/paper/2104.10353/) (2021)
- [TLogic: Temporal Logical Rules for Explainable Link Forecasting on Temporal Knowledge Graphs](https://mlsanity.com/paper/2112.08025/) (2021)
- [Search from History and Reason for Future: Two-stage Reasoning on Temporal Knowledge Graphs](https://mlsanity.com/paper/2106.00327/) (2021)
- [A Comprehensive Survey of Dynamic Graph Neural Networks: Models, Frameworks, Benchmarks, Experiments and Challenges](https://mlsanity.com/paper/2405.00476/) (2024)
- [What is Event Knowledge Graph: A Survey](https://mlsanity.com/paper/2112.15280/) (2021)
- [Chain-of-History Reasoning for Temporal Knowledge Graph Forecasting](https://mlsanity.com/paper/2402.14382/) (2024)
- [Learning to Evolve on Dynamic Graphs](https://mlsanity.com/paper/2111.07032/) (2021)
- [Improving Few-Shot Inductive Learning on Temporal Knowledge Graphs using Confidence-Augmented Reinforcement Learning](https://mlsanity.com/paper/2304.00613/) (2023)
- [Selective Temporal Knowledge Graph Reasoning](https://mlsanity.com/paper/2404.01695/) (2024)
- [Inductive Reasoning for Temporal Knowledge Graphs with Emerging Entities](https://mlsanity.com/paper/2604.10164/) (2026)
- [CEP3: Community Event Prediction with Neural Point Process on Graph](https://mlsanity.com/paper/2205.10624/) (2022)
- [CHRONOS: Temporally-Aware Multi-Agent Coordination for Evolving Data Marketplaces](https://mlsanity.com/paper/2605.23887/) (2026)

## Abstract (arXiv)

Knowledge graph reasoning is a critical task in natural language processing. The task becomes more challenging on temporal knowledge graphs, where each fact is associated with a timestamp. Most existing methods focus on reasoning at past timestamps and they are not able to predict facts happening in the future. This paper proposes Recurrent Event Network (RE-NET), a novel autoregressive architecture for predicting future interactions. The occurrence of a fact (event) is modeled as a probability distribution conditioned on temporal sequences of past knowledge graphs. Specifically, our RE-NET employs a recurrent event encoder to encode past facts and uses a neighborhood aggregator to model the connection of facts at the same timestamp. Future facts can then be inferred in a sequential manner based on the two modules. We evaluate our proposed method via link prediction at future times on five public datasets. Through extensive experiments, we demonstrate the strength of RENET, especially on multi-step inference over future timestamps, and achieve state-of-the-art performance on all five datasets. Code and data can be found at https://github.com/INK-USC/RE-Net.