alirezahajibagheri/LinkPredictionPackage
Contains code for link prediction algorithms in single and multiple layer networks
LinkPredictionPackage
The aim of this project is to study the structure of different types of networks that emerge in massively multi-player online games. Travian is a popular browser-based real-time strategy game with more than 5 million players. Players form alliances of up to 60 members under a leader or a leadership team. Each game cycle lasts a fixed period (a few months).
This network dataset was designed for studying multiplex network problems, in particular community detection since the ground truth for alliance membership is available. It was extracted from data from a study of virtual organizations conducted by researchers across several institutions, including the University of Arkansas. Data was collected from a 3x server (with a 3.5 month game cycle) based in Germany. These networks are from a 30 day game period. For more information you can visit our webpage at: ial.eecs.ucf.edu/travian.php
Citation
If you want to use the network data please cite one of the following papers:
"Conflict and Communication in Massively-Multiplayer Online Games" Alireza Hajibagheri, Kiran Lakkaraju, Gita Sukthankar, Rolf T. Wigand, and Nitin Agrawal. Proceedings of the International Conference on Social Computing, Behavioral-Cultural Modeling, and Prediction. 2015.
"Using Massively Multiplayer Online Game Data to Analyze the Dynamics of Social Interactions" Alireza Hajibagheri, Gita Sukthankar, Kiran Lakkaraju, Hamidreza Alvari, Rolf T Wigand, Nitin Agarwal. Social Interaction in Virtual Worlds. 2017.
We have developed a Python toolkit for multiplex link prediction on this dataset. The algorithms are described in these papers: