19 citations · 57 across the 7 of their papers we have counts for
7 papers
Inductive Meta-path Learning for Schema-complex Heterogeneous Information Networks
Shixuan Liu, Changjun Fan, Kewei Cheng +4
Heterogeneous Information Networks (HINs) are information networks with multiple types of nodes and edges. The concept of meta-path, i.e., a sequence of entity types and relation t…
Reply to: Deep reinforced learning heuristic tested on spin-glass ground states: The larger picture
Changjun Fan, Mutian Shen, Zohar Nussinov +3
We wish to thank Stefan Boettcher for prompting us to further check and highlight the accuracy and scaling of our results. Here we provide a comprehensive response to the Comment w…
Finding spin glass ground states through deep reinforcement learning
Changjun Fan, Mutian Shen, Zohar Nussinov +3
Spin glasses are disordered magnets with random interactions that are, generally, in conflict with each other. Finding the ground states of spin glasses is not only essential for t…
Bio-JOIE: Joint Representation Learning of Biological Knowledge Bases
Junheng Hao, Chelsea Ju, Muhao Chen +3
The widespread of Coronavirus has led to a worldwide pandemic with a high mortality rate. Currently, the knowledge accumulated from different studies about this virus is very limit…
Multilingual Knowledge Graph Completion via Ensemble Knowledge Transfer
Xuelu Chen, Muhao Chen, Changjun Fan +3
Predicting missing facts in a knowledge graph (KG) is a crucial task in knowledge base construction and reasoning, and it has been the subject of much research in recent works usin…
Learning to Identify High Betweenness Centrality Nodes from Scratch: A Novel Graph Neural Network Approach
Changjun Fan, Li Zeng, Yuhui Ding +3
Betweenness centrality (BC) is one of the most used centrality measures for network analysis, which seeks to describe the importance of nodes in a network in terms of the fraction…
Embedding Uncertain Knowledge Graphs
Xuelu Chen, Muhao Chen, Weijia Shi +2
Embedding models for deterministic Knowledge Graphs (KG) have been extensively studied, with the purpose of capturing latent semantic relations between entities and incorporating t…