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Found 5 papers, 2 papers with code
HeteroMILE: a Multi-Level Graph Representation Learning Framework for Heterogeneous Graphs
To address this issue, we propose a Multi-Level Embedding framework of nodes on a heterogeneous graph (HeteroMILE) - a generic methodology that allows contemporary graph embedding methods to scale to large graphs.
FairMILE: Towards an Efficient Framework for Fair Graph Representation Learning
FairMILE is a multi-level paradigm that can efficiently learn graph representations while enforcing fairness and preserving utility.
Sepsis Prediction with Temporal Convolutional Networks
We design and implement a temporal convolutional network model to predict sepsis onset.
FairEGM: Fair Link Prediction and Recommendation via Emulated Graph Modification
As machine learning becomes more widely adopted across domains, it is critical that researchers and ML engineers think about the inherent biases in the data that may be perpetuated by the model.
SYSML: StYlometry with Structure and Multitask Learning: Implications for Darknet Forum Migrant Analysis
Darknet market forums are frequently used to exchange illegal goods and services between parties who use encryption to conceal their identities.
