4.8k citations · 5.9k across the 13 of their papers we have counts for
13 papers
GraphVF: Controllable Protein-Specific 3D Molecule Generation with Variational Flow
Fang Sun, Zhihao Zhan, Hongyu Guo +2
Designing molecules that bind to specific target proteins is a fundamental task in drug discovery. Recent models leverage geometric constraints to generate ligand molecules that bi…
A*Net: A Scalable Path-based Reasoning Approach for Knowledge Graphs
Zhaocheng Zhu, Xinyu Yuan, Mikhail Galkin +4
Reasoning on large-scale knowledge graphs has been long dominated by embedding methods. While path-based methods possess the inductive capacity that embeddings lack, their scalabil…
Ekar: An Explainable Method for Knowledge Aware Recommendation
Weiping Song, Zhijian Duan, Ziqing Yang +3
This paper studies recommender systems with knowledge graphs, which can effectively address the problems of data sparsity and cold start. Recently, a variety of methods have been d…
Learning Hierarchical Representations of Electronic Health Records for Clinical Outcome Prediction
Luchen Liu, Haoran Li, Zhiting Hu +4
Clinical outcome prediction based on the Electronic Health Record (EHR) plays a crucial role in improving the quality of healthcare. Conventional deep sequential models fail to cap…
End-to-end Learning for Short Text Expansion
Jian Tang, Yue Wang, Kai Zheng +1
Effectively making sense of short texts is a critical task for many real world applications such as search engines, social media services, and recommender systems. The task is part…
Roaming across the Castle Tunnels: an Empirical Study of Inter-App Navigation Behaviors of Android Users
Ziniu Hu, Yun Ma, Qiaozhu Mei +1
Mobile applications (a.k.a., apps), which facilitate a large variety of tasks on mobile devices, have become indispensable in our everyday lives. Accomplishing a task may require t…
Less is More: Learning Prominent and Diverse Topics for Data Summarization
Jian Tang, Cheng Li, Ming Zhang +1
Statistical topic models efficiently facilitate the exploration of large-scale data sets. Many models have been developed and broadly used to summarize the semantic structure in ne…
Context-aware Natural Language Generation with Recurrent Neural Networks
Jian Tang, Yifan Yang, Sam Carton +2
This paper studied generating natural languages at particular contexts or situations. We proposed two novel approaches which encode the contexts into a continuous semantic represen…
Identity-sensitive Word Embedding through Heterogeneous Networks
Jian Tang, Meng Qu, Qiaozhu Mei
Most existing word embedding approaches do not distinguish the same words in different contexts, therefore ignoring their contextual meanings. As a result, the learned embeddings o…
Visualizing Large-scale and High-dimensional Data
Jian Tang, Jingzhou Liu, Ming Zhang +1
We study the problem of visualizing large-scale and high-dimensional data in a low-dimensional (typically 2D or 3D) space. Much success has been reported recently by techniques tha…
PTE: Predictive Text Embedding through Large-scale Heterogeneous Text Networks
Jian Tang, Meng Qu, Qiaozhu Mei
Unsupervised text embedding methods, such as Skip-gram and Paragraph Vector, have been attracting increasing attention due to their simplicity, scalability, and effectiveness. Howe…
LINE: Large-scale Information Network Embedding
Jian Tang, Meng Qu, Mingzhe Wang +3
This paper studies the problem of embedding very large information networks into low-dimensional vector spaces, which is useful in many tasks such as visualization, node classifica…
"Look Ma, No Hands!" A Parameter-Free Topic Model
Jian Tang, Ming Zhang, Qiaozhu Mei
It has always been a burden to the users of statistical topic models to predetermine the right number of topics, which is a key parameter of most topic models. Conventionally, auto…