标题： 通过边缘锚点增强网络的稳定性 显示英文标题

标题： Enhance Stability of Network by Edge Anchor

摘要： 随着在线社交网络的快速增长，加强其稳定性已成为一项关键的研究重点。 本研究旨在识别对社区稳定性有显著影响的相关关系。 在本文中，我们引入并探讨了锚定三明治度增强问题，通过锚定一些边来增强网络的整体用户参与度。 具体而言，对于给定的图$G$和预算$b$，我们的目标是识别$b$条边，其锚定可使三明治度增益最大化，即图$G$中所有边的三明治度累积增量。 我们建立了该问题的NP难性。 为了解决这个问题，我们引入了一个贪心框架，该框架迭代地选择当前最佳的边。 为了适应更大的网络，我们首先提出了一种向上路径方法，以限制潜在的三明治度增量边。 结合支持检查策略，这种方法能够高效计算锚定一条边的三明治度增益。 然后，我们设计了一个分类树结构，通过根据边的三明治度对它们进行组织，从而在每次迭代中减少冗余计算。 我们在8个真实网络上进行了大量实验，以验证所提出的模型和方法的效率和有效性。 显示英文摘要

摘要： With the rapid growth of online social networks, strengthening their stability has emerged as a key research focus. This study aims to identify influential relationships that significantly impact community stability. In this paper, we introduce and explore the anchor trussness reinforcement problem to reinforce the overall user engagement of networks by anchoring some edges. Specifically, for a given graph $G$ and a budget $b$, we aim to identify $b$ edges whose anchoring maximizes the trussness gain, which is the cumulative increment of trussness across all edges in $G$. We establish the NP-hardness of the problem. To address this problem, we introduce a greedy framework that iteratively selects the current best edge. To scale for larger networks, we first propose an upward-route method to constrain potential trussness increment edges. Augmented with a support check strategy, this approach enables the efficient computation of the trussness gain for anchoring one edge. Then, we design a classification tree structure to minimize redundant computations in each iteration by organizing edges based on their trussness. We conduct extensive experiments on 8 real-world networks to validate the efficiency and effectiveness of the proposed model and methods.