4 citations · 8 across the 9 of their papers we have counts for
14 papers
Interpretable Node Representation with Attribute Decoding
Xiaohui Chen, Xi Chen, Liping Liu
Variational Graph Autoencoders (VGAEs) are powerful models for unsupervised learning of node representations from graph data. In this work, we systematically analyze modeling node…
Towards Accurate Subgraph Similarity Computation via Neural Graph Pruning
Linfeng Liu, Xu Han, Dawei Zhou +1
Subgraph similarity search, one of the core problems in graph search, concerns whether a target graph approximately contains a query graph. The problem is recently touched by neura…
Ensemble Spectral Prediction (ESP) Model for Metabolite Annotation
Xinmeng Li, Hao Zhu, Li-ping Liu +1
A key challenge in metabolomics is annotating measured spectra from a biological sample with chemical identities. Currently, only a small fraction of measurements can be assigned i…
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…