Automating Graph Workflows with OpenPajek ScriptsAutomating graph workflows speeds up repetitive network-analysis tasks, improves reproducibility, and makes large-scale experiments feasible. This article explains how to design, write, and run scripts for OpenPajek to automate data import, cleaning, analysis, visualization, and export. It covers script structure, common commands, practical examples, error handling, and tips for integrating OpenPajek scripts into larger pipelines.
Why automate OpenPajek workflows?
Manual interaction with GUI tools is fine for exploration, but automation is essential when you:
- need to process many networks or time-sliced snapshots,
- want reproducible analyses,
- must run long pipelines overnight or on remote servers,
- want to integrate network processing into data-science workflows.
OpenPajek scripting lets you run the same sequence of operations on multiple files without human intervention, saving time and reducing mistakes.
OpenPajek scripting basics
OpenPajek scripts are text files with commands that OpenPajek executes sequentially. Typical script tasks:
- load graphs (from Pajek .net, edge lists, CSV)
- convert or clean data (remove self-loops, consolidate parallel edges)
- compute measures (degree, centralities, components, clustering)
- layout and visualize graphs (apply layouts, adjust labels/colors)
- export results (graphs, images, tables, metric files)
Script syntax mirrors Pajek command names and menu operations. Save scripts with a .paj or .netlist extension (depending on your OpenPajek version and preferences). Use full paths or relative paths when referencing files.
Common commands and patterns
Below are frequently used command categories with examples (pseudo-commands; adapt to your OpenPajek version’s exact syntax):
- File operations
- NET LOAD “path/to/file.net”
- EXPORT NET “path/to/output.net”
- Vertex/edge filters and cleaning
- DELETE LOOPS
- SIMPLIFY (merge parallel edges)
- DELETE ISOLATED VERTICES
- Measures and analysis
- CALC DEGREES
- CENTRALITY BETWEENNESS
- COMPONENTS
- Layout and drawing
- LAYOUT FRUCHTERMAN_REINGOLD
- SET VERTEX SIZE 5
- SET EDGE COLOR “gray”
- Exporting results
- EXPORT PICTURE “path/to/image.png” WIDTH 1200 HEIGHT 800
- SAVE VECTOR “path/to/plot.svg”
- EXPORT VERTEX ATTRIBUTES “path/to/attributes.csv”
Use variables and loops if supported; otherwise create external shell or Python wrappers to iterate over files and call OpenPajek with different script inputs.
Example 1 — Batch compute centralities for a folder of networks
This pattern is useful when you have many .net files and want degree and betweenness for each vertex saved as CSVs.
- Create a template script compute_centralities.paj with placeholders:
- LOAD NET “{INPUT}”
- CALC DEGREES
- EXPORT VERTEX ATTRIBUTES “{OUTPUT_DIR}/{BASENAME}_degrees.csv”
- CENTRALITY BETWEENNESS
- EXPORT VERTEX ATTRIBUTES “{OUTPUT_DIR}/{BASENAME}_betweenness.csv”
- QUIT
- Use a shell or Python wrapper to replace placeholders and call OpenPajek for each input file.
Shell (bash) example:
for f in /data/nets/*.net; do base=$(basename "$f" .net) sed "s|{INPUT}|$f|; s|{OUTPUT_DIR}|/data/results|; s|{BASENAME}|$base|" compute_centralities.paj > run_${base}.paj openpajek-cli run_${base}.paj done Example 2 — Cleaning and visualizing a dynamic network
Task: Import edge list, remove loops, aggregate edges by weight, apply layout, export image.
Script steps:
- IMPORT EDGELIST “edges.csv” FORMAT CSV
- DELETE LOOPS
- AGGREGATE EDGES BY WEIGHT SUM
- LAYOUT KAMADA_KAWAI
- SET VERTEX SIZE BY NORMALIZED DEGREE
- EXPORT PICTURE “visualization.png” WIDTH 1600 HEIGHT 1000
- QUIT
Adjust attribute-mapping commands to scale vertex sizes and color by attribute ranges.
Error handling and logging
- Redirect OpenPajek console output to log files to inspect errors and warnings.
- Validate input files before processing (check for corrupt lines, missing headers).
- Add checkpoints in scripts where intermediate results are exported; this helps resume after failures.
- When using wrappers, check exit codes and retry or skip problematic files with notifications.
Integrating OpenPajek scripts with other tools
- Use Python (networkx, pandas) to pre-process data or post-process exported attribute CSVs.
- Use cron or Airflow to schedule recurring jobs.
- Containerize OpenPajek and your wrappers with Docker for reproducible environments.
- Combine with command-line image tools (imagemagick) for automated figure composition.
Performance tips
- Reduce graph size by removing irrelevant nodes/edges early.
- Prefer binary or compressed formats if supported to speed I/O.
- For very large graphs, compute approximations (e.g., approximate betweenness) or sample subgraphs.
- Run parallel jobs across multiple CPU cores or nodes, keeping I/O load in mind.
Best practices
- Version control your scripts and configuration files.
- Keep a manifest file listing input files, parameters, and outputs for reproducibility.
- Use clear, consistent naming for exported files that include timestamps or parameter tags.
- Document assumptions (e.g., directed vs undirected, weight semantics) inside scripts as comments.
Sample small script (conceptual)
Below is a concise conceptual example demonstrating loading, cleaning, computing degree, and exporting. Adapt syntax to your OpenPajek version.
NET LOAD "projects/sample.net" DELETE LOOPS SIMPLIFY CALC DEGREES EXPORT VERTEX ATTRIBUTES "projects/sample_degrees.csv" LAYOUT FRUCHTERMAN_REINGOLD EXPORT PICTURE "projects/sample_visual.png" WIDTH 1200 HEIGHT 800 QUIT Final notes
Automating OpenPajek workflows reduces manual effort, increases reproducibility, and enables scaling. Start by identifying repetitive tasks, draft simple scripts, and use wrappers for batching. Monitor logs, version control scripts, and integrate OpenPajek into broader data pipelines for the best results.
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