11 citations · 25 across the 5 of their papers we have counts for
5 papers · 1 filter
Semi-Supervised Deep Learning for Multiplex Networks
Anasua Mitra, Priyesh Vijayan, Ranbir Sanasam +3
Multiplex networks are complex graph structures in which a set of entities are connected to each other via multiple types of relations, each relation representing a distinct layer.…
Ego-GNNs: Exploiting Ego Structures in Graph Neural Networks
Dylan Sandfelder, Priyesh Vijayan, William L. Hamilton
Graph neural networks (GNNs) have achieved remarkable success as a framework for deep learning on graph-structured data. However, GNNs are fundamentally limited by their tree-struc…
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…
Fusion Graph Convolutional Networks
Priyesh Vijayan, Yash Chandak, Mitesh M. Khapra +2
Semi-supervised node classification in attributed graphs, i.e., graphs with node features, involves learning to classify unlabeled nodes given a partially labeled graph. Label pred…
HOPF: Higher Order Propagation Framework for Deep Collective Classification
Priyesh Vijayan, Yash Chandak, Mitesh M. Khapra +2
Given a graph where every node has certain attributes associated with it and some nodes have labels associated with them, Collective Classification (CC) is the task of assigning la…