Heterogeneous Graph Network Embedding for Sentiment Analysis on Social Media
Sign Up to like & getrecommendations! Published in 2021 at "Cognitive Computation"
DOI: 10.1007/s12559-020-09793-7
Abstract: Nowadays, more people are used to express their attitudes on different entities in online social networks, forming user-to-entity sentiment links. These sentiment links imply positive or negative semantics. Most of current user sentiment analysis literature… read more here.
Keywords: network; sentiment; network embedding; graph network ... See more keywords
Data Mining and Graph Network Deep Learning for Band Gap Prediction in Crystalline Borate Materials.
Sign Up to like & getrecommendations! Published in 2023 at "Inorganic chemistry"
DOI: 10.1021/acs.inorgchem.3c00233
Abstract: Crystalline borates are an important class of functional materials with wide applications in photocatalysis and laser technologies. Obtaining their band gap values in a timely and precise manner is a great challenge in material design… read more here.
Keywords: band; network deep; graph network; deep learning ... See more keywords
Interpretable Graph-Network-Based Machine Learning Models via Molecular Fragmentation.
Sign Up to like & getrecommendations! Published in 2023 at "Journal of chemical theory and computation"
DOI: 10.1021/acs.jctc.2c01308
Abstract: Chemists have long benefitted from the ability to understand and interpret the predictions of computational models. With the current shift to more complex deep learning models, in many situations that utility is lost. In this… read more here.
Keywords: graph network; interpretable graph; machine learning; learning models ... See more keywords
Crystal structure prediction by combining graph network and optimization algorithm.
Sign Up to like & getrecommendations! Published in 2022 at "Nature communications"
DOI: 10.1038/s41467-022-29241-4
Abstract: Crystal structure prediction is a long-standing challenge in condensed matter and chemical science. Here we report a machine-learning approach for crystal structure prediction, in which a graph network (GN) is employed to establish a correlation… read more here.
Keywords: optimization algorithm; crystal structure; optimization; graph network ... See more keywords
Relational Conduction Graph Network for Intelligent Fault Diagnosis of Rotating Machines Under Small Fault Samples
Sign Up to like & getrecommendations! Published in 2023 at "IEEE Transactions on Instrumentation and Measurement"
DOI: 10.1109/tim.2023.3268665
Abstract: Fault samples obtained in real-world environment are limited, which makes it hard to diagnose faults of rotating machines (RM) accurately by using the existing intelligent diagnosis methods. To solve the issue above, a new relational… read more here.
Keywords: graph network; fault; real world; fault samples ... See more keywords