Lipschitz Continuous Algorithms for Graph Problems
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It has been widely observed in the machine learning community that a small perturbation to the input can cause a large change in the prediction of a trained model, and such phenomena have been intensively studied in the machine learning community under the name of adversarial attacks. Because graph algorithms also are widely used for decision making and knowledge discovery, it is important to design graph algorithms that are robust against adversarial attacks. In this study, we consider the Lipschitz continuity of algorithms as a robustness measure and initiate a systematic study of the Lipschitz continuity of algorithms for (weighted) graph problems. Depending on how we embed the output solution to a metric space, we can think of several Lipschitzness notions. We mainly consider the one that is invariant under scaling of weights, and we provide Lipschitz continuous algorithms and lower bounds for the minimum spanning tree problem, the shortest path problem, and the maximum weight matching problem. In particular, our shortest path algorithm is obtained by first designing an algorithm for unweighted graphs that are robust against edge contractions and then applying it to the unweighted graph constructed from the original weighted graph. Then, we consider another Lipschitzness notion induced by a natural mapping that maps the output solution to its characteristic vector. It turns out that no Lipschitz continuous algorithm exists for this Lipschitz notion, and we instead design algorithms with bounded pointwise Lipschitz constants for the minimum spanning tree problem and the maximum weight bipartite matching problem. Our algorithm for the latter problem is based on an LP relaxation with entropy regularization.
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