On the Complexity of the Bipartite Polarization Problem: from Neutral to Highly Polarized Discussions
This work provides insights into computational complexity for social network analysis, but it is incremental as it builds on prior methods for instance generation and complexity assessment.
The paper investigates the Bipartite Polarization Problem, an optimization task for finding highly polarized bipartitions in graphs representing debates, and finds that instances with higher polarization are easier to solve, with complexity correlating with a polarization parameter.
The Bipartite Polarization Problem is an optimization problem where the goal is to find the highest polarized bipartition on a weighted and labelled graph that represents a debate developed through some social network, where nodes represent user's opinions and edges agreement or disagreement between users. This problem can be seen as a generalization of the maxcut problem, and in previous work approximate solutions and exact solutions have been obtained for real instances obtained from Reddit discussions, showing that such real instances seem to be very easy to solve. In this paper, we investigate further the complexity of this problem, by introducing an instance generation model where a single parameter controls the polarization of the instances in such a way that this correlates with the average complexity to solve those instances. The average complexity results we obtain are consistent with our hypothesis: the higher the polarization of the instance, the easier is to find the corresponding polarized bipartition.