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- W2620403746 abstract "The maximum flow problem is a classical optimization problem with a wide range of applications. Nowadays, it is successfully applied in social network analysis for link spam detection, web communities identification, and others. In such applications, flow networks are used to model connections among web pages, online voting systems, web communities, P2P and other distributed systems. Thus, networks are highly dynamic, that is, subject to frequent updates. While many efficient algorithms for the maximum flow problem have been proposed over the years, they are designed to work with static networks, and thus they need to recompute a new solution from scratch every time an update occurs. Such approaches are impractical in scenarios like the aforementioned ones, where updates are frequent. To overcome these limitations, this paper proposes efficient incremental algorithms for maintaining the maximum flow in dynamic networks. Our approach identifies and acts only on the affected portions of the network. We evaluate our approach on different families of datasets, comparing it against state-of-the-art algorithms, showing that our technique is significantly faster and can efficiently handle networks with millions of vertices and tens of millions of edges." @default.
- W2620403746 created "2017-06-05" @default.
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- W2620403746 date "2017-04-03" @default.
- W2620403746 modified "2023-09-27" @default.
- W2620403746 title "Incremental maximum flow computation on evolving networks" @default.
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- W2620403746 doi "https://doi.org/10.1145/3019612.3019816" @default.
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