In this article, we design algorithms to maintain approximate core values in dynamic hypergraphs. This notion has been well studied for normal graphs in both static and dynamic setting. We generalize the problem to hypergraphs when edges can be inserted or deleted by an adversary. We consider two dynamic scenarios. In the first case, there are only insertions; and in the second case, there can be both insertions and deletions. In either case, the update time is poly-logarithmic in the number of nodes, with the insertion-only case boasting a better approximation ratio. We also perform extensive experiments on large real-world datasets, which demonstrate the accuracy and efficiency of our algorithms.

Fully Dynamic Approximate k-Core Decomposition in Hypergraphs / Sun, Bintao; Hubert Chan, T. -H.; Sozio, Mauro. - In: ACM TRANSACTIONS ON KNOWLEDGE DISCOVERY FROM DATA. - ISSN 1556-472X. - 14:4(2020), pp. 1-21. [10.1145/3385416]

Fully Dynamic Approximate k-Core Decomposition in Hypergraphs

Mauro Sozio
2020

Abstract

In this article, we design algorithms to maintain approximate core values in dynamic hypergraphs. This notion has been well studied for normal graphs in both static and dynamic setting. We generalize the problem to hypergraphs when edges can be inserted or deleted by an adversary. We consider two dynamic scenarios. In the first case, there are only insertions; and in the second case, there can be both insertions and deletions. In either case, the update time is poly-logarithmic in the number of nodes, with the insertion-only case boasting a better approximation ratio. We also perform extensive experiments on large real-world datasets, which demonstrate the accuracy and efficiency of our algorithms.
2020
Network algorithms; Theory of computation; Online algorithms; Streaming, sublinear and near linear time algorithms; Core values, dynamic network, hypergraphs
Fully Dynamic Approximate k-Core Decomposition in Hypergraphs / Sun, Bintao; Hubert Chan, T. -H.; Sozio, Mauro. - In: ACM TRANSACTIONS ON KNOWLEDGE DISCOVERY FROM DATA. - ISSN 1556-472X. - 14:4(2020), pp. 1-21. [10.1145/3385416]
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Utilizza questo identificativo per citare o creare un link a questo documento: https://hdl.handle.net/11385/251383
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