Hands-on temporal graphs
In this section, we will introduce representative examples of the machine learning approaches described in the previous sections for dealing with temporal graphs. We will offer a general understanding of how these approaches work and provide examples of their implementation using publicly available frameworks.
Temporal matrix factorization
Concerning the matrix factorization class of approaches, the Temporal Matrix Factorization (TMF) model by Yu et al. (2017) is a method used for temporal link prediction, particularly in dynamic network scenarios. This technique leverages matrix factorization with temporal dynamics to model the evolution of links in a dynamic network over time.
To exemplify this method, we adopted the implementation provided in the publicly available OpenTLP library (https://github.com/KuroginQin/OpenTLP). It integrates an encoder-decoder architecture, where the encoder learns model parameters through matrix factorization, and...
