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The inference of outcomes in dynamic processes from structural features ofsystems is a crucial endeavor in network science. Recent research has suggesteda machine learning-based approach for the interpretation of dynamic patternsemerging in complex network systems. The hypothesis is applied in this studytowards showing opinions can be classified in the voter model with the use oftopological features from complex networks. An analysis was first performedwith 2 (0 or 1) opinions from a voter model and extended for 3 and then 4opinions from the same model. A breakdown of the key network topologicalfeatures for the estimation is provided and network metrics are ranked in theorder of importance with high accuracy. Our generalized approach is applicableto dynamical processes running on complex networks. This study is a steptowards the application of machine learning methods to studies of dynamicalpatterns from complex network systems.

Machine learning for the prediction of voter model opinions through complex network structures