Abstract
AbstractGeneralized maximum flow problem is a generalization of the traditional maximum flow problem, where each edge e has gain factor γ(e). When f(e) units of flow enter edge e = (u, v) at u, then γ(e) f(e) units of flow arrive at v. Since relation extraction, which is an important application of the problem, uses large networks such as Wikipedia and DBLP, the computation time to solve the problem is important. However, conventional algorithms for the problem are expensive and do not scale to large graph. Therefore, we propose approximation algorithms based on greedy augmentation and a heuristic initial flow calculation. The experimental result shows that our algorithms are two orders of magnitude faster than a conventional algorithm.KeywordsInformation NetworkGeneralize MaximumGain FactorLarge GraphRelation ExtractionThese keywords were added by machine and not by the authors. This process is experimental and the keywords may be updated as the learning algorithm improves.
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