Predicting community evolution based on time series modeling

Abstract

Communities in real life are usually dynamic and community structures evolve over time. Detecting community evolution provides insight into the underlying behavior of the network. A growing body of study is devoted in studying the dynamics of communities in evolving social networks. Most of them provide an event-based framework to characterize and track the community evolution. A part of these studies take a step further and provide a predictive model of the events by exploiting community features. However, the proposed models require the community extraction and computing the community features relevant to the time point to be predicted. In this paper, we proposed a new approach for predicting events by estimating feature values related to the communities in a given network. An event-based framework is used to characterize community behavior patterns. Then, a time series ARIMA model is used to predict how particular community features will change in the following time period. Distinct time windows are examined in constituting and analyzing time series. Our proposed approach efficiently tracks similar communities and identifies events over time. Furthermore, community feature values are forecasted with an acceptable error rate. Event prediction using forecasted feature values substantially match up with actual events.