AI 中文总结
针对优先连接网络中异常顶点的检测难题，提出结合迭代参数估计与似然框架的方法，可准确估计相关参数，且异常在网络演化中期时检测效果最优。
AI 中文摘要
优先连接（PA）网络是一种被广泛用于刻画现实网络增长动态的模型，在该模型中，新加入的顶点更倾向于连接到度数更高的已有顶点。本文考虑如下场景：某一异常顶点在某个时间点出现，其连边会受到由参数β控制的额外连接优势，而普通顶点则继续遵循参数为δ的PA机制。由于PA网络中度增长的高变异性，且异常在网络演化后期出现时可用信息有限，检测此类异常颇具挑战性。我们提出了一种迭代参数估计流程以及基于似然的检测框架。仿真结果表明，该流程能对参数β和δ进行准确估计。检测性能取决于异常出现的时间：在网络演化中期出现的异常最易被可靠检测，而极早期和极晚期的异常则仍具挑战性，尤其是当参数β较小时。
英文摘要
Preferential attachment (PA) network is a widely used model for capturing the growth dynamics of real-world networks, in which newly arriving vertices are more likely to connect to existing vertices with higher degrees. In this paper, we consider a setting in which an anomalous vertex appears at some time point and receives edges with an additional attachment advantage governed by a parameter $β$, while ordinary vertices continue to follow the PA mechanism with parameter $δ$. Detecting such anomalies is challenging due to the high variability in degree growth in PA networks and the limited information available when the anomaly arrives late in the network evolution. We propose an iterative parameter estimation procedure together with a likelihood-based detection framework. Simulation results show that the proposed procedure provides accurate estimation of the parameters $β$ and $δ$. Detection performance depends on the time of anomaly occurrence: anomalies arising at midway stages of the network evolution are detected most reliably, whereas very early and late anomalies remain challenging, particularly when the parameter $β$ is small.
