Edge Weight Prediction in Weighted Signed Networks
By Srijan Kumar, Francesca Spezzano, Venkatramanan Siva Subrahmanian, Christos Faloutsos. 2016
582 citations recorded by the source corpus.
Subfields: Artificial Intelligence, Sociology and Political Science, Statistical and Nonlinear Physics, Graph Learning
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Citations in the corpus
- Fast Graph Representation Learning with PyTorch Geometric (2019, background): “(2017), and the temporal datasets BitcoinOTC (Kumar et al., 2016), ICEWS (Boschee et al.”
- SHINE: Signed Heterogeneous Information Network Embedding for Sentiment Link Prediction (2017, background): “ sentiment links. On the other hand, the complexity of sentiment generation and the sparsity of sentiment links make it hard for algorithms to achieve desirable performance. Recently, several studies [12, 14, 31, 35] propose methods to solve the problem of predicting signed links. However, they rely”
- PGM-Explainer: Probabilistic Graphical Model Explanations for Graph Neural Networks (2020, methodology): “We use the Trust weighted signed networks Bitcoin-Alpha and Bitcoin-OTC datasets [35].”
- REV2: Fraudulent User Prediction in Rating Platforms (2018, background): “• Bitcoin OTC is a user-to-user trust network of Bitcoin users trading using OTC platform [14].”
- GMNN: Graph Markov Neural Networks (2019, methodology): “For link classification, we construct two datasets from the Bitcoin Alpha and the Bitcoin OTC datasets (Kumar et al., 2016; 2018) respectively.”