60 citations · 66 across the 2 of their papers we have counts for
2 papers
cs.LG2019★ 60 cited
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…
physics.soc-ph2017★ 6 cited
Entrograms and coarse graining of dynamics on complex networks
Mauro Faccin, Michael T. Schaub, Jean-Charles Delvenne
Using an information theoretic point of view, we investigate how a dynamics acting on a network can be coarse grained through the use of graph partitions. Specifically, we are inte…