Graph-based Semi-Supervised & Active Learning for Edge Flows

Understand

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 predict the flows on the remaining edges.
• To this end, we develop a computational framework that imposes certain constraints on the overall flows, such as (approximate) flow conservation.
• These constraints render our approach different from classical graph-based SSL for vertex labels, which posits that tightly connected nodes share similar labels and leverages the graph structure accordingly to extrapolate from a few vertex labels to the unlabeled vertices.