142 citations · 323 across the 16 of their papers we have counts for
18 papers
Federated Graph Representation Learning using Self-Supervision
Susheel Suresh, Danny Godbout, Arko Mukherjee +3
Federated graph representation learning (FedGRL) brings the benefits of distributed training to graph structured data while simultaneously addressing some privacy and compliance co…
Lightweight Compositional Embeddings for Incremental Streaming Recommendation
Mengyue Hang, Tobias Schnabel, Longqi Yang +1
Most work in graph-based recommender systems considers a {\em static} setting where all information about test nodes (i.e., users and items) is available upfront at training time.…
Breaking the Limit of Graph Neural Networks by Improving the Assortativity of Graphs with Local Mixing Patterns
Susheel Suresh, Vinith Budde, Jennifer Neville +2
Graph neural networks (GNNs) have achieved tremendous success on multiple graph-based learning tasks by fusing network structure and node features. Modern GNN models are built upon…
Adversarial Graph Augmentation to Improve Graph Contrastive Learning
Susheel Suresh, Pan Li, Cong Hao +1
Self-supervised learning of graph neural networks (GNN) is in great need because of the widespread label scarcity issue in real-world graph/network data. Graph contrastive learning…
A Hybrid Model for Learning Embeddings and Logical Rules Simultaneously from Knowledge Graphs
Susheel Suresh, Jennifer Neville
The problem of knowledge graph (KG) reasoning has been widely explored by traditional rule-based systems and more recently by knowledge graph embedding methods. While logical rules…
Cluster-Based Social Reinforcement Learning
Mahak Goindani, Jennifer Neville
Social Reinforcement Learning methods, which model agents in large networks, are useful for fake news mitigation, personalized teaching/healthcare, and viral marketing, but it is c…