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Found 9 papers, 4 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.
PolicyClusterGCN: Identifying Efficient Clusters for Training Graph Convolutional Networks
Specifically, the subgraph-based sampling approaches such as ClusterGCN and GraphSAINT have achieved state-of-the-art performance on the node classification tasks.
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.
MultiBiSage: A Web-Scale Recommendation System Using Multiple Bipartite Graphs at Pinterest
MultiBiSage can capture the graph structure of multiple bipartite graphs to learn high-quality pin embeddings.
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.
A Machine Learning Model for Nowcasting Epidemic Incidence
Due to delay in reporting, the daily national and statewide COVID-19 incidence counts are often unreliable and need to be estimated from recent data.
Towards Quantifying the Distance between Opinions
Similarly, in a supervised setting, our opinion distance measure achieves considerably better accuracy (up to 20% increase) compared to extant approaches that rely on text similarity, stance similarity, and sentiment similarity
Network Representation Learning: Consolidation and Renewed Bearing
An important area of research that has emerged over the last decade is the use of graphs as a vehicle for non-linear dimensionality reduction in a manner akin to previous efforts based on manifold learning with uses for downstream database processing, machine learning and visualization.
MILE: A Multi-Level Framework for Scalable Graph Embedding
We employ our framework on several popular graph embedding techniques and conduct embedding for real-world graphs.
