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Found 10 papers, 9 papers with code
Semi-Supervised Deep Learning for Multiplex Networks
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
Graph neural networks (GNNs) have achieved remarkable success as a framework for deep learning on graph-structured data.
On Incorporating Structural Information to improve Dialogue Response Generation
This requires capturing structural, sequential and semantic information from the conversation context and the background resources.
Understanding Dynamic Scenes using Graph Convolution Networks
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 by a moving monocular camera.
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A Unified Non-Negative Matrix Factorization Framework for Semi-Supervised Learning on Graphs
We propose a Semi-Supervised Learning (SSL) methodology that explicitly encodes different necessary priors to learn efficient representations for nodes in a network.
Towards Accurate Vehicle Behaviour Classification With Multi-Relational Graph Convolutional Networks
Understanding on-road vehicle behaviour from a temporal sequence of sensor data is gaining in popularity.
Influence maximization in unknown social networks: Learning Policies for Effective Graph Sampling
A serious challenge when finding influential actors in real-world social networks is the lack of knowledge about the structure of the underlying network.
Network Representation Learning: Consolidation and Renewed Bearing
An important area of research that has emerged over the last decade is the use of graphs as a vehicle for non-linear dimensionality reduction in a manner akin to previous efforts based on manifold learning with uses for downstream database processing, machine learning and visualization.
Fusion Graph Convolutional Networks
State-of-the-art models for node classification on such attributed graphs use differentiable recursive functions that enable aggregation and filtering of neighborhood information from multiple hops.
HOPF: Higher Order Propagation Framework for Deep Collective Classification
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 labels to every unlabeled node using information from the node as well as its neighbors.
