activity
20202022
most citedxERTE: Explainable Reasoning on Temporal Knowledge Graphs for Forecasting Future Links

19 citations · 24 across the 4 of their papers we have counts for

collaborators

5 papers

cs.LG2022

Continuous Temporal Graph Networks for Event-Based Graph Data

Jin Guo, Zhen Han, Zhou Su +3

There has been an increasing interest in modeling continuous-time dynamics of temporal graph data. Previous methods encode time-evolving relational information into a low-dimension…

cs.LG20211 cited

TimeTraveler: Reinforcement Learning for Temporal Knowledge Graph Forecasting

Haohai Sun, Jialun Zhong, Yunpu Ma +2

Temporal knowledge graph (TKG) reasoning is a crucial task that has gained increasing research interest in recent years. Most existing methods focus on reasoning at past timestamps…

cs.LG20204 cited

DyERNIE: Dynamic Evolution of Riemannian Manifold Embeddings for Temporal Knowledge Graph Completion

Zhen Han, Yunpu Ma, Peng Chen +1

There has recently been increasing interest in learning representations of temporal knowledge graphs (KGs), which record the dynamic relationships between entities over time. Tempo…

cs.LG202019 cited

xERTE: Explainable Reasoning on Temporal Knowledge Graphs for Forecasting Future Links

Zhen Han, Peng Chen, Yunpu Ma +1

Modeling time-evolving knowledge graphs (KGs) has recently gained increasing interest. Here, graph representation learning has become the dominant paradigm for link prediction on t…

cs.LG2020

Graph Hawkes Neural Network for Forecasting on Temporal Knowledge Graphs

Zhen Han, Yunpu Ma, Yuyi Wang +2

The Hawkes process has become a standard method for modeling self-exciting event sequences with different event types. A recent work has generalized the Hawkes process to a neurall…