26 citations · 65 across the 9 of their papers we have counts for
6 papers · 1 filter
HeteroMILE: a Multi-Level Graph Representation Learning Framework for Heterogeneous Graphs
Yue Zhang, Yuntian He, Saket Gurukar +1
Heterogeneous graphs are ubiquitous in real-world applications because they can represent various relationships between different types of entities. Therefore, learning embeddings…
PolicyClusterGCN: Identifying Efficient Clusters for Training Graph Convolutional Networks
Saket Gurukar, Shaileshh Bojja Venkatakrishnan, Balaraman Ravindran +1
Graph convolutional networks (GCNs) have achieved huge success in several machine learning (ML) tasks on graph-structured data. Recently, several sampling techniques have been prop…
FairMILE: Towards an Efficient Framework for Fair Graph Representation Learning
Yuntian He, Saket Gurukar, Srinivasan Parthasarathy
Graph representation learning models have demonstrated great capability in many real-world applications. Nevertheless, prior research indicates that these models can learn biased r…
MultiBiSage: A Web-Scale Recommendation System Using Multiple Bipartite Graphs at Pinterest
Saket Gurukar, Nikil Pancha, Andrew Zhai +5
Graph Convolutional Networks (GCN) can efficiently integrate graph structure and node features to learn high-quality node embeddings. These embeddings can then be used for several…
FairEGM: Fair Link Prediction and Recommendation via Emulated Graph Modification
Sean Current, Yuntian He, Saket Gurukar +1
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…
Network Representation Learning: Consolidation and Renewed Bearing
Saket Gurukar, Priyesh Vijayan, Aakash Srinivasan +9
Graphs are a natural abstraction for many problems where nodes represent entities and edges represent a relationship across entities. An important area of research that has emerged…