122 citations · 258 across the 19 of their papers we have counts for
6 papers · 1 filter
Shape-conditioned 3D Molecule Generation via Equivariant Diffusion Models
Ziqi Chen, Bo Peng, Srinivasan Parthasarathy +1
Ligand-based drug design aims to identify novel drug candidates of similar shapes with known active molecules. In this paper, we formulated an in silico shape-conditioned molecule…
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…
M2: Mixed Models with Preferences, Popularities and Transitions for Next-Basket Recommendation
Bo Peng, Zhiyun Ren, Srinivasan Parthasarathy +1
Next-basket recommendation considers the problem of recommending a set of items into the next basket that users will purchase as a whole. In this paper, we develop a novel mixed mo…