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Predicting the Evolution of Communities with Online Inductive Logic Programming
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25th International Symposium on Temporal Representation and Reasoning (TIME 2018)
Series: Leibniz International Proceedings in Informatics (LIPIcs)
Conference: International Symposium on Temporal Representation and Reasoning (TIME) - License:
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- Publication Date: 2018-10-08
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George Athanasopoulos, George Paliouras, Dimitrios Vogiatzis, Grigorios Tzortzis, and Nikos Katzouris. Predicting the Evolution of Communities with Online Inductive Logic Programming. In 25th International Symposium on Temporal Representation and Reasoning (TIME 2018). Leibniz International Proceedings in Informatics (LIPIcs), Volume 120, pp. 4:1-4:20, Schloss Dagstuhl – Leibniz-Zentrum für Informatik (2018)
https://doi.org/10.4230/LIPIcs.TIME.2018.4
https://doi.org/10.4230/LIPIcs.TIME.2018.4
Abstract
In the recent years research on dynamic social network has increased, which is also due to the availability of data sets from streaming media. Modeling a network's dynamic behaviour can be performed at the level of communities, which represent their mesoscale structure. Communities arise as a result of user to user interaction. In the current work we aim to predict the evolution of communities, i.e. to predict their future form. While this problem has been studied in the past as a supervised learning problem with a variety of classifiers, the problem is that the "knowledge" of a classifier is opaque and consequently incomprehensible to a human. Thus we have employed first order logic, and in particular the event calculus to represent the communities and their evolution. We addressed the problem of predicting the evolution as an online Inductive Logic Programming problem (ILP), where the issue is to learn first order logical clauses that associate evolutionary events, and particular Growth, Shrinkage, Continuation and Dissolution to lower level events. The lower level events are features that represent the structural and temporal characteristics of communities. Experiments have been performed on a real life data set form the Mathematics StackExchange forum, with the OLED framework for ILP. In doing so we have produced clauses that model both short term and long term correlations.
Subject Classification
ACM Subject Classification
- Human-centered computing → Social network analysis
- Computing methodologies → Machine learning
- Computing methodologies → Online learning settings
- Computing methodologies → Inductive logic learning
Keywords
- Social Network Analysis
- Community Evolution Prediction
- Machine Learning
- Inductive Logic Programming
- Event Calculus
- Online Learning
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References
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