60 citations · 73 across the 8 of their papers we have counts for
5 papers · 1 filter
Graph Belief Propagation Networks
Junteng Jia, Cenk Baykal, Vamsi K. Potluru +1
With the wide-spread availability of complex relational data, semi-supervised node classification in graphs has become a central machine learning problem. Graph neural networks are…
A Unifying Generative Model for Graph Learning Algorithms: Label Propagation, Graph Convolutions, and Combinations
Junteng Jia, Austin R. Benson
Semi-supervised learning on graphs is a widely applicable problem in network science and machine learning. Two standard algorithms -- label propagation and graph neural networks --…
Residual Correlation in Graph Neural Network Regression
Junteng Jia, Austin R. Benson
A graph neural network transforms features in each vertex's neighborhood into a vector representation of the vertex. Afterward, each vertex's representation is used independently f…
Graph-based Semi-Supervised & Active Learning for Edge Flows
Junteng Jia, Michael T. Schaub, Santiago Segarra +1
We present a graph-based semi-supervised learning (SSL) method for learning edge flows defined on a graph. Specifically, given flow measurements on a subset of edges, we want to pr…
Neural Jump Stochastic Differential Equations
Junteng Jia, Austin R. Benson
Many time series are effectively generated by a combination of deterministic continuous flows along with discrete jumps sparked by stochastic events. However, we usually do not hav…