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Temporal Graph Analysis with TGX

Razieh Shirzadkhani, Shenyang Huang, Elahe Kooshafar, Reihaneh Rabbany, Farimah Poursafaei

TL;DR

TGX addresses the gap in temporal-network tooling by providing a Python package that ingests CTDGs and discretizes them into a sequence of snapshots (G_t). It includes a dataset loader for eleven built-in datasets and eight external TGB datasets, plus support for custom edge-lists loaded via read_edgelist. The package offers flexible time discretization, node-subsampling, and statistics such as total nodes/edges over time, average node activity, and average degree over time, as well as TEA and TET visualizations and novelty, reoccurrence, and surprise indices. These capabilities enable robust analysis and evaluation of temporal graph learning tasks across domains like social and citation networks, with an open-source repository for ongoing community-driven development.

Abstract

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.

Temporal Graph Analysis with TGX

TL;DR

TGX addresses the gap in temporal-network tooling by providing a Python package that ingests CTDGs and discretizes them into a sequence of snapshots (G_t). It includes a dataset loader for eleven built-in datasets and eight external TGB datasets, plus support for custom edge-lists loaded via read_edgelist. The package offers flexible time discretization, node-subsampling, and statistics such as total nodes/edges over time, average node activity, and average degree over time, as well as TEA and TET visualizations and novelty, reoccurrence, and surprise indices. These capabilities enable robust analysis and evaluation of temporal graph learning tasks across domains like social and citation networks, with an open-source repository for ongoing community-driven development.

Abstract

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.
Paper Structure (8 sections, 5 figures)

This paper contains 8 sections, 5 figures.

Table of Contents

  1. Temporal Graph Preliminaries
  2. Features
  3. Temporal Graph Loader
  4. Time Discretization
  5. Graph Sub-sampling
  6. Temporal Graph Statistics
  7. Temporal Patterns
  8. Conclusion

Figures (5)

  • Figure 1: Example plot illustrating the total number of nodes and edges per timestamp in the MOOC dataset discretized into 30 time intervals.
  • Figure 2: The average node degree per timestamp plots for the MOOC dataset, discretized into 10, 50 and 100 intervals. The red line indicates the average degree over all timestamps.
  • Figure 3: TEA plot examples for (a) LastFM and (b) Social Evo. datasets, discretized monthly and weekly, respectively.
  • Figure 4: TET plot examples for (a) LastFM and (b) Social Evo. datasets, discretized monthly and weekly respectively.
  • Figure 5: TET plot examples for tgbn-reddit dataset. (a) Data discretized into 10 intervals. (b) Sub-sampled graph from 1000 random nodes, discretized into 50 intervals.