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.

社交网络的稳定性作为网络持有者和参与者的重要指标已被广泛研究。现有的关于增强网络的工作集中在局部视图上，例如，锚定\(k\)核心问题旨在用固定输入k扩大\(k\)核心的大小. 尽管如此，以全局方式加强社交网络更有希望：考虑网络中每个用户（顶点）的参与。由于用户的核心已被验证为捕获用户参与度的“最佳实践”，我们在本文中提出并研究了锚定核心问题：锚定少量顶点以最大化核心增益（核心的总增量）网络中的所有顶点。我们证明该问题是 NP-hard 问题，并表明它比现有的局部视图问题更具挑战性。提出了一种有效的贪婪算法，其中包含修剪搜索空间和重用中间结果的新技术。该算法还通过新颖的图分区策略扩展到分布式环境，以确保每台机器的计算独立性。