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Anchored coreness: efficient reinforcement of social networks


The stability of a social network has been widely studied as an important indicator for both the network holders and the participants. Existing works on reinforcing networks focus on a local view, e.g., the anchored \(k\)-core problem aims to enlarge the size of the \(k\)-core with a fixed input k. Nevertheless, it is more promising to reinforce a social network in a global manner: considering the engagement of every user (vertex) in the network. Since the coreness of a user has been validated as the “best practice” for capturing user engagement, we propose and study the anchored coreness problem in this paper: anchoring a small number of vertices to maximize the coreness gain (the total increment of coreness) of all the vertices in the network. We prove the problem is NP-hard and show it is more challenging than the existing local-view problems. An efficient greedy algorithm is proposed with novel techniques on pruning search space and reusing the intermediate results. The algorithm is also extended to distributed environment with a novel graph partition strategy to ensure the computing independency of each machine. Extensive experiments on real-life data demonstrate that our model is effective for reinforcing social networks and our algorithms are efficient.

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Fan Zhang is supported by NSFC62002073. Xuemin Lin is supported by ARC DP200101338. Wenjie Zhang is supported by ARC DP210101393 and ARC DP200101116. Ying Zhang is supported by FT170100128 and ARC DP180103096.

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Linghu, Q., Zhang, F., Lin, X. et al. Anchored coreness: efficient reinforcement of social networks. The VLDB Journal (2021).

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  • Social network
  • User engagement
  • Network stability
  • Core decomposition
  • Distributed algorithm