Dynamic network analysis

In contrast, SNA statistical tools focus on single or at most two mode data and facilitate the analysis of only one type of link at a time.

Latent space models (Sarkar and Moore, 2005)[3] and agent-based simulation are often used to examine dynamic social networks (Carley et al., 2009).

Properties change over time; nodes can adapt: A company's employees can learn new skills and increase their value to the network; or, capture one terrorist and three more are forced to improvise.

DNA adds the element of a network's evolution and considers the circumstances under which change is likely to occur.

Complex information about object relationships can be effectively condensed into low-dimensional embeddings in a latent space.

[6] In essence, the stability of the system defines its dynamics, while misalignment signifies irrelevant changes in the latent space.

The matter of stability and alignment of dynamic embeddings holds significant importance in various tasks reliant on temporal changes within the latent space.

These tasks encompass future metadata prediction, temporal evolution, dynamic visualization, and obtaining average embeddings, among others.