Event network analyzer (eventnet): statistical analysis of networks of relational events.
A relational event represents time-stamped interaction ( who does when what to whom). Examples include a person sending an message to another person, a Wikipedia user editing a Wikipedia article, a customer buying a certain product, or a country signing an agreement with another country. A relational hyperevent represents time-stamped interaction among varying and potentially large numbers of "nodes". Examples include meeting events, team work (e.g., coauthoring of papers), multi-cast or "one-to-many" communication such as email, or papers citing lists of other papers in their references.
Relational (hyper) event models R(H)EM seek to model sequences of such interaction events to uncover or test factors explaining that some nodes interact at a higher rate than others. While the core statistical models are well-established in survival or event-history analysis (e.g., Cox regression) and are implemented in numerous free and commercial statistical software packages, typical explanatory variables of REM and RHEM are novel, network-specific, and sometimes conceptually and computationally demanding to understand and implement. This gap is filled by the eventnet software.
See the tutorials in the eventnet wiki to get started.
+++ Release of eventnet one +++
Eventnet one (Version 1.0 or later), latest release in eventnet-1.1.jar, comes with three important changes.
- The functionality for dyadic relational event models (REM) and for relational hyperevent models (RHEM) is now provided in a single JAR file (
eventnet-1.0.jaror later). - RHEM can now also be specified purely in the graphical user interface (GUI).
- RHEM effects have been completely reorganized. The number of different core types of RHEM statistics could be reduced but a more efficent use of the arguments of statistics actually provides a much larger variation of possible RHEM effects than in prior versions. Note that because of this reorganization, configuration files from versions prior to 1.0 will most likely no longer work with eventnet one. (Note, however, that the JAR files of prior versions are still available at https://github.com/juergenlerner/eventnet/tree/master/jars/old_versions-0.x.)
RHEM effects available in Version 1.0 or later are exhaustively listed and discussed in the Reference guide on RHEM effects. However, users new to eventnet are recommended to first have a look at the more basic tutorials linked from the eventnet wiki.
To use eventnet, download the file eventnet-1.1.jar and start the program by either of:
- double-clicking on the JAR file opens the eventnet graphical user interface (GUI)
- typing the command
java -jar eventnet-x.y.jaropens the eventnet GUI - typing the command
java -jar eventnet-x.y.jar <configuration_filename.xml>, where<configuration_filename.xml>is the name of a file containing a fully specified eventnet configuration, executes this configuration without opening the GUI.
See the eventnet wiki for online tutorials.
Eventnet is written in java and needs the java runtime environment (JRE), Java 8 (JRE version 1.8) or higher. An alternative to the JRE by Oracle is the Eclipse Temurin JRE.
Source code (.java files) are in eventnet-1.1-src.jar.
Eventnet is distributed under the GNU General Public License v3.0.
Citation:
- Lerner and Lomi (2020). Reliability of relational event model estimates under sampling: how to fit a relational event model to 360 million dyadic events. Network Science, 8(1):97-135. (DOI: 10.1017/nws.2019.57)
- Lerner and Lomi (2023). Relational hyperevent models for polyadic interaction networks. Journal of the Royal Statistical Society: Series A. (https://doi.org/10.1093/jrsssa/qnac012)
The usual place to get started are the online tutorials in the eventnet wiki.
Moreover, training workshops or courses introducing eventnet are offered at the following conferences or summerschools.
Planned next events (details will follow)
- REM/RHEM course at the POLNET school 13-17 January 2025, Barcelona, Spain.
- RHEM course at the International "Winter Course," Theory, Methods and Applications of Personal Networks, UAB Egolab, Barcelona, Spain. February 3-7, 2025.
Past training events
- REM beyond dyads: relational hyperevent modeling with eventnet (beginners and advanced), within the INSNA Sunbelt Conference 2024, Edinburgh. 24-30 June 2024.
- International "Winter Course," Theory, Methods and Applications of Personal Networks, UAB Egolab, Barcelona, Spain. February 5-9, 2024.
- REM beyond dyads: relational hyperevent modeling with eventnet (beginners and advanced), within the European Conference on Social Networks (EUSN 2023), Ljubljana, Slovenia. 4-8 September 2023.
- Relational Event Models (REMs) for the Analysis of Social Networks: A Hands-on Introduction, part of the NUSC Summer School in Network and Data Science, Greenwich, UK. 19th - 23rd June 2023.
- International "Winter Course," Theory, Methods and Applications of Personal Networks, Barcelona, Spain. February 6-10, 2023.
- REM beyond dyads: relational hyperevent modeling with eventnet, within the European Conference on Social Networks (EUSN 2022), Greenwich, UK. 12-16 September 2022.
- Relational Event Models (REMs) for the Analysis of Social Networks: A Hands-on Introduction, part of the NUSC Summer School in Network and Data Science, Greenwich, UK. Mon 20th - Sat 25th June 2022.
- International "Winter Course," Theory, Methods and Applications of Personal Networks, Barcelona, Spain. February 7-11, 2022.
- European Conference on Social Networks, Zurich, Switzerland. September 12, 2019.
- Sunbelt Social Networks Conference, Montreal, Canada. June 19, 2019.
- Relational event models for the analysis of social networks, University of Exeter, Business School. June 3-4, 2019.
There is a commented literature list of own work on relational event models (REM) or relational hyperevent models (RHEM), using the eventnet software or (published or unpublished) predecessor software in its empirical analysis. The listed papers are expected to complement the eventnet tutorials in providing more formal details, a better embedding into related work, and a more thorough discussion of objectives and contributions. They may also point to further illustrating use cases of REM and RHEM.
Jürgen Lerner (juergen.lerner@gmail.com)
We acknowledge financial support from Deutsche Forschungsgemeinschaft (DFG) under Project No. 321869138. From 2017 to 2020: "Statistical analysis of structural balance in signed social networks" (LE 2237/2-1) and from 2021 to 2025: "Statistical analysis of time-stamped multi-actor events in social networks" (LE 2237/2-2).