Sun Dec 8th through Sat the 14th, 2019 at Vancouver Convention Center
This paper presents a theoretical analysis of approximation algorithms for correlation clustering. It gives approximation algorithms for minimizing the l_q norm of the disagreement vector on arbitrary graphs and provides better approximation algorithms on complete graphs. The work is based on previous known results, which dealt with minimizing l_1 or l_inf. There is also lack of code and experiments, which makes it hard to judge how the algorithms would work on larger datasets/problems. The authors are advised to review their paper in light of the reviewers' comments.