1 research outputs found
Predicting the Evolution of Social Networks: Optimal Time Window Size for Increased Accuracy
This study investigates the data preparation process
for predictive modelling of the evolution of complex networked
systems, using an e–mail based social network as an example. In
particular, we focus on the selection of optimal time window
size for building a time series of network snapshots, which
forms the input of chosen predictive models. We formulate this
issue as a constrained multi–objective optimization problem,
where the constraints are specific to a particular application and
predictive algorithm used. The optimization process is guided
by the proposed Windows Incoherence Measures, defined as
averaged Jensen-Shannon divergences between distributions of a
range of network characteristics for the individual time windows
and the network covering the whole considered period of time.
The experiments demonstrate that the informed choice of window
size according to the proposed approach allows to boost the
prediction accuracy of all examined prediction algorithms, and
can also be used for optimally defining the prediction problems
if some flexibility in their definition is allowed