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Found 7 papers, 0 papers with code
Nondiagonal Mixture of Dirichlet Network Distributions for Analyzing a Stock Ownership Network
Block modeling is widely used in studies on complex networks.
Gaussian Hierarchical Latent Dirichlet Allocation: Bringing Polysemy Back
Compared with latent Dirichlet allocation, Gaussian latent Dirichlet allocation is limited in the sense that it does not capture the polysemy of a word such as ``bank.''
Prediction of ESG Compliance using a Heterogeneous Information Network
Negative screening is one method to avoid interactions with inappropriate entities.
Learning Topic Models by Neighborhood Aggregation
Topic models are frequently used in machine learning owing to their high interpretability and modular structure.
Semi-supervised Graph Embedding Approach to Dynamic Link Prediction
We propose a simple discrete time semi-supervised graph embedding approach to link prediction in dynamic networks.
A New Approach to Building the Interindustry Input--Output Table
By exploiting interfirm buyer--seller network data, we show that the problem of estimating the interdependence of industries is similar to the problem of uncovering the latent block structure in network science literature.
High quality topic extraction from business news explains abnormal financial market volatility
We show that the whole landscape of news that affect stock price movements can be automatically summarized via simple regularized regressions between trading activity and news information pieces decomposed, with the help of simple topic modeling techniques, into their "thematic" features.
