Models for Capturing Temporal Smoothness in Evolving Networks for Learning Latent Representation of Nodes

Understand

In a dynamic network, the neighborhood of the vertices evolve across different temporal snapshots of the network.

• Accurate modeling of this temporal evolution can help solve complex tasks involving real-life social and interaction networks.
• However, existing models for learning latent representation are inadequate for obtaining the representation vectors of the vertices for different time-stamps of a dynamic network in a meaningful way.
• In this paper, we propose latent representation learning models for dynamic networks which overcome the above limitation by considering two different kinds of temporal smoothness: (i) retrofitted, and (ii) linear transformation.