Sensitive Analysis Of Timeframe Type And Size Impact On Community Evolution Prediction

One of the most interesting issues in the field of social network analysis is community evolution prediction in dynamic social networks. To start with, the dynamic network is split into a series of timeframes, each one containing interactions aggregated over a time period such as a month, a day or an hour. Splitting the network into timeframes is of crucial importance to capture the right communities' temporal evolution before predicting their future. Our paper investigates the problem of choosing the appropriate scale for network splitting which would improve the prediction. The experiments we conducted on Facebook and Higgs Twitter datasets offer strong empirical evidence of the usefulness of considering the appropriate network splitting as a first step in predicting community evolution in dynamic social networks.