Carnegie Mellon University

CASOS Center

Center for Computational Analysis of Social and Organizational Systems

CASOS Center


ORA-LITE is a dynamic meta-network assessment and analysis tool developed by CASOS at Carnegie Mellon.  It contains hundreds of social network, dynamic network metrics, trail metrics, procedures for grouping nodes, identifying local patterns, comparing and contrasting networks, groups, and individuals from a dynamic meta-network perspective. ORA-LITE has been used to examine how networks change through space and time,  contains procedures for moving back and forth between trail data (e.g. who was where when) and network data (who is connected to whom,  who is connected to where …),  and has a variety of geo-spatial network metrics, and change detection techniques.  ORA-LITE can handle multi-mode, multi-plex, multi-level networks.  It can identify key players, groups and vulnerabilities, model network changes over time, and perform COA analysis.  Although all metrics available in ORA-LITE have been tested with large networks (10^6 nodes per 5 entity classes); ORA-LITE is itself limited to a maximum of 2,000 nodes per entity class. So you can have 2000 agents, 2000 organizations, and so on. The Professional version of ORA which is available from Netanomics ( has no limit on the number of nodes. Distance based, algorithmic, and statistical procedures for comparing and contrasting networks are part of this toolkit.

Based on network theory, social psychology, operations research, and management theory a series of measures of “criticality” have been developed at CMU.  Just as critical path algorithms can be used to locate those tasks that are critical from a project management perspective, the ORA-LITE algorithms can find those people, types of skills or knowledge and tasks that are critical from a performance and information security perspective.  Each of the measures we have developed are calculated by ORA-LITE on the basis of network data like that in the following table. 


ORA-LITE can be applied both within a traditional organization or on covert networks.

Version of ORA-LITE (v3.0.9.163) offers the following improvements:
released August 17, 2023

1. Importers a. Extended reddit importer b. Telegram importer 2. Telegram and Reddit reports 3. Show data over time a. Extended to allow attributes as well as measures to be shown 4. Improved date handling 5. Enabled undo for the nodeset editor 6. Key Entity report: a. User can now choose the same Trend Chart parameters as are in the Changes in Key Entities b. Improved the Attribute Analysis presentation, and added a bar chart 7. BEND a. Targets of back and neutralize maneuvers added b. Separated out combined “Maneuvered” and “Maneuvered-Upon” spider charts into separate charts and added a description for each. The legend adds no additional information, and so I did not move it to the top. 8. Visualizer a. Improved navigation b. New color node procedure 9. Editor and Filtering a. Improved the Meta-Network Transformation function to “Normalize URL” so that any attribute in a nodeset of type URL can be converted 10. Add the ability to save out Ranked Entity report tables as node attributes of the input meta-network (this includes Twitter’s Super spreader and Super Friend) 11. Add a key for agent labels in the tables (+ = verified, * = News source, etc.). There were many reports that did not have the node markers; these have been fixed 12. Improved filtering capabilities 13. Added the ability to automatically create nested sub-directories for report output. The default is to save a single meta-network report to a sub-directory with the name of the report. In addition, the user can choose to create sub-directories of the form: a. D:/Trident Juncture Before/Key Entities Ranking/report.html b. D:/Trident Juncture Day Of/Key Entities Ranking /report.html c. D:/Trident Juncture After/Key Entities Ranking /report.html Or d. D:/Key Entities Ranking /Trident Juncture Before/report.html e. D:/Key Entities Ranking /Trident Juncture Day Of/report.html f. D:/Key Entities Ranking /Trident Juncture After/report.html Or – just using report, or just using meta network ID. This should give users the needed flexibility to organize the voluminous output from ORA 14. Added the Projected Modularity to the Locate Groups report. This is the “Community Prototype” attributes. 15. Improvements to the Key Change report: a. User can now select the number of top-ranked entities to chart b. User can select line and/or bar charts 16. Improved Help 17. Miscellaneous bug fixes

Hardware Requirements

  • CPU with 500 megahertz or higher processor clock speed recommended (3 Ghz is recommended for large datasets)
    * Intel Pentium/Celeron family, or AMD K6/Athlon/Duron family, or compatible processor recommended
  • 512 MB of RAM or higher recommended (1 GB preferred)
  • 500 MB of available hard disk space

Notes on Multi-Core Processors

  • ORA-LITE can take advantage of all the cores in your machine.  All
    computationally intense measures are multi-threaded.

System Interaction

  • ORA-LITE has a java front end and a C++ backend. It does NOT touch the system registry.
  • Altman, Neal & Carley, Kathleen M. (2022). ORA User's Guide 2022. Carnegie Mellon University, School of Computer Science, Institute for Software Research, Technical Report CMU-ISR-22-107, [pdf]
  • Carley, Kathleen M. (2014). ORA: A Toolkit for Dynamic Network Analysis and Visualization, In Reda Alhajj and Jon Rokne (Eds.) Encyclopedia of Social Network Analysis and Mining, Springer.
  • Ujawary-Gil, Anna (2019). Organizational Network Analysis: Auditing Intangible Resources. 1st Edition. Routledge. [link]
  • ORA Quickstart Guide
  • Using ORA with Social Media
  • Description of ORA


This work was supported in part by:
The Office of Naval Research (ONR)
- United States Navy Grant (ONR - MURI) No. N000140811186
- United States Navy Grant No. 9620.1.1140071 on Dynamic Network Analysis
- United States Navy Grant No. 1681.12.1140053 on Adaptive Architecture for Command and Control
- The Department of Defense
The Defense Advanced Research Projects Agency (DARPA) INSIGHT project DAAH01-03-C-R111
National Aeronautics and Space Administration (NASA)
The Army Research Lab (ARL), and ARL-CTA
The National Science Foundation under NSF 0100999 doctoral dissertation grant, NSF ITR 0218466 NSF ITR/IM IIS-0081219, NSF KDI IIS-9980109, NSF 043 7239, NSF 045 2598, NSF 045 2487, and NSF IGERT 9972762 for research and training in CASOS.
The Air Force Office of Scientific Research (AFOSR) under Grant 600322GRGMASON, Computational Modeling of Cultural Dimensions in Adversary Organization (MURI), with George Mason University.
The Department of Defense (DOD)
The Office of the Chief of Naval Operations (Op-Nav)
The Office of National Drug Control Policy (ONDCP)
Army Research Institute (ARI)
Army Research Office (ARO)

The views and conclusions contained in this document are those of the authors and should not be interpreted as representing the official policies, either expressed or implied, of the Office of Naval Research, the Department of Defense, the Army Research Lab, NASA, the National Science Foundation or the U.S. government.