11 citations · 20 across the 4 of their papers we have counts for
9 papers
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…
On Incorporating Structural Information to improve Dialogue Response Generation
Nikita Moghe, Priyesh Vijayan, Balaraman Ravindran +1
We consider the task of generating dialogue responses from background knowledge comprising of domain specific resources. Specifically, given a conversation around a movie, the task…
Understanding Dynamic Scenes using Graph Convolution Networks
Sravan Mylavarapu, Mahtab Sandhu, Priyesh Vijayan +3
We present a novel Multi-Relational Graph Convolutional Network (MRGCN) based framework to model on-road vehicle behaviors from a sequence of temporally ordered frames as grabbed b…
Towards Accurate Vehicle Behaviour Classification With Multi-Relational Graph Convolutional Networks
Sravan Mylavarapu, Mahtab Sandhu, Priyesh Vijayan +3
Understanding on-road vehicle behaviour from a temporal sequence of sensor data is gaining in popularity. In this paper, we propose a pipeline for understanding vehicle behaviour f…
Influence maximization in unknown social networks: Learning Policies for Effective Graph Sampling
Harshavardhan Kamarthi, Priyesh Vijayan, Bryan Wilder +2
A serious challenge when finding influential actors in real-world social networks is the lack of knowledge about the structure of the underlying network. Current state-of-the-art m…