Temporal Graph Analysis with TGX
Razieh Shirzadkhani Shenyang Huang Elahe Kooshafar Reihaneh Rabbany Farimah Poursafaei
Real-world networks, with their evolving relations, are best captured as
temporal graphs. However, existing software libraries are largely designed for
static graphs where the dynamic nature of temporal graphs is ignored. Bridging
this gap, we introduce TGX, a Python package specially designed for analysis of
temporal networks that encompasses an automated pipeline for data loading, data
processing, and analysis of evolving graphs. TGX provides access to eleven
built-in datasets and eight external Temporal Graph Benchmark (TGB) datasets as
well as any novel datasets in the .csv format. Beyond data loading, TGX
facilitates data processing functionalities such as discretization of temporal
graphs and node subsampling to accelerate working with larger datasets. For
comprehensive investigation, TGX offers network analysis by providing a diverse
set of measures, including average node degree and the evolving number of nodes
and edges per timestamp. Additionally, the package consolidates meaningful
visualization plots indicating the evolution of temporal patterns, such as
Temporal Edge Appearance (TEA) and Temporal Edge Trafficc (TET) plots. The TGX
package is a robust tool for examining the features of temporal graphs and can
be used in various areas like studying social networks, citation networks, and
tracking user interactions. We plan to continuously support and update TGX
based on community feedback. TGX is publicly available on:
https://github.com/ComplexData-MILA/TGX.