An Implementation, Empirical Evaluation and Proposed Improvement for
  Bidirectional Splitting Method for Argumentation Frameworks under Stable
  Semantics
  Abstract argumentation frameworks are formal systems that facilitate
obtaining conclusions from non-monotonic knowledge systems. Within such a
system, an argumentation semantics is defined as a set of arguments with some
desired qualities, for example, that the elements are not in conflict with each
other. Splitting an argumentation framework can efficiently speed up the
computation of argumentation semantics. With respect to stable semantics, two
methods have been proposed to split an argumentation framework either in a
unidirectional or bidirectional fashion. The advantage of bidirectional
splitting is that it is not structure-dependent and, unlike unidirectional
splitting, it can be used for frameworks consisting of a single strongly
connected component. Bidirectional splitting makes use of a minimum cut. In
this paper, we implement and test the performance of the bidirectional
splitting method, along with two types of graph cut algorithms. Experimental
data suggest that using a minimum cut will not improve the performance of
computing stable semantics in most cases. Hence, instead of a minimum cut, we
propose to use a balanced cut, where the framework is split into two
sub-frameworks of equal size. Experimental results conducted on bidirectional
splitting using the balanced cut show a significant improvement in the
performance of computing semantics.
