81 citations · 127 across the 22 of their papers we have counts for
6 papers · 1 filter
Boost-RS: Boosted Embeddings for Recommender Systems and its Application to Enzyme-Substrate Interaction Prediction
Xinmeng Li, Li-ping Liu, Soha Hassoun
Despite experimental and curation efforts, the extent of enzyme promiscuity on substrates continues to be largely unexplored and under documented. Recommender systems (RS), which a…
Ladder Polynomial Neural Networks
Li-Ping Liu, Ruiyuan Gu, Xiaozhe Hu
Polynomial functions have plenty of useful analytical properties, but they are rarely used as learning models because their function class is considered to be restricted. This work…
Order Matters: Probabilistic Modeling of Node Sequence for Graph Generation
Xiaohui Chen, Xu Han, Jiajing Hu +2
A graph generative model defines a distribution over graphs. One type of generative model is constructed by autoregressive neural networks, which sequentially add nodes and edges t…
Stochastic Iterative Graph Matching
Linfeng Liu, Michael C. Hughes, Soha Hassoun +1
Recent works leveraging Graph Neural Networks to approach graph matching tasks have shown promising results. Recent progress in learning discrete distributions poses new opportunit…
DiaKG: an Annotated Diabetes Dataset for Medical Knowledge Graph Construction
Dejie Chang, Mosha Chen, Chaozhen Liu +9
Knowledge Graph has been proven effective in modeling structured information and conceptual knowledge, especially in the medical domain. However, the lack of high-quality annotated…
Modeling Graph Node Correlations with Neighbor Mixture Models
Linfeng Liu, Michael C. Hughes, Li-Ping Liu
We propose a new model, the Neighbor Mixture Model (NMM), for modeling node labels in a graph. This model aims to capture correlations between the labels of nodes in a local neighb…