ESND：一种基于嵌入的符号网络拆解框架
ESND: An Embedding-based Framework for Signed Network Dismantling
AI总结：
针对现有网络拆解方法多适配无符号网络、未考虑关系性质的问题，提出基于嵌入的ESND算法，经多数据集验证其拆解性能优于基线且稳定，还发现负边占比高的网络更易拆解。
摘要：
网络拆解旨在通过移除特定的节点或边集合来最大化网络的瓦解程度，已应用于不同领域的各类任务中，例如打击犯罪组织、延缓谣言传播以及阻断病毒传播。当前大多数网络拆解方法都是为无符号网络量身定制的，这类方法仅考虑节点间的连接，而不评估关系的性质，比如友好/敌对、增强/抑制以及信任/不信任。我们在此提出一种基于嵌入的算法，即ESND，用于解决符号网络拆解问题。该算法通常迭代执行以下四个步骤：巨组件检测、网络嵌入、节点聚类以及移除节点选择。为了说明ESND的有效性和稳定性，我们在六个符号网络数据集以及零模型上开展了大量实验，并将我们方法的性能与基线方法进行对比。实验结果一致表明，所提出的ESND优于基线方法，并且在网络结构变化时表现出稳定的性能。此外，我们通过ESND检验了符号比例对网络鲁棒性的影响，观察到负边占比高的网络通常比正边占比高的网络更易拆解。
英文摘要：
Network dismantling aims to maximize the disintegration of a network by removing a specific set of nodes or edges and is applied to various tasks in diverse domains, such as cracking down on crime organizations, delaying the propagation of rumors, and blocking the transmission of viruses. Most of the current network dismantling methods are tailored for unsigned networks, which only consider the connection between nodes without evaluating the nature of the relationships, such as friendship/hostility, enhancing/repressing, and trust/distrust. We here propose an embedding-based algorithm, namely ESND, to solve the signed network dismantling problem. The algorithm generally iterates the following four steps, i.e., giant component detection, network embedding, node clustering, and removal node selection. To illustrate the efficacy and stability of ESND, we conduct extensive experiments on six signed network datasets as well as null models, and compare the performance of our method with baselines. Experimental results consistently show that the proposed ESND is superior to the baselines and displays stable performance with the change in the network structure. Additionally, we examine the impact of sign proportions on network robustness via ESND, observing that networks with a high ratio of negative edges are generally easier to dismantle than networks with high positive edges.
机构、总结、中文摘要均由AI分析而来，可能存在错误，请以论文正文为准。
