HodgeNet: Graph Neural Networks for Edge Data

Understand

Networks and network processes have emerged as powerful tools for modeling social interactions, disease propagation, and a variety of additional dynamics driven by relational structures.

• Recently, neural networks have been generalized to process data on graphs, thus being able to learn from the aforementioned network processes achieving cutting-edge performance in traditional tasks such as node classification and link prediction.
• However, these methods have all been formulated in a way suited only to data on the nodes of a graph.
• The application of these techniques to data supported on the edges of a graph, namely flow signals, has not been explored in detail.