论文标题
随时间变化网络中变更点检测的潜在进化模型
Latent Evolution Model for Change Point Detection in Time-varying Networks
论文作者
论文摘要
基于图的变更点检测（CPD）在发现时间变化网络中的异常图中起着不可替代的作用。尽管已经提出了几种技术来通过确定目标网络与先前连续网络之间存在显着差异来检测变化点，但它们忽略了网络的自然发展。在实践中，随着时间的流逝，社交网络，流量网络和评级网络等现实图表不断发展。考虑到这个问题，我们将问题视为预测任务，并通过潜在的进化模型提出了一种新型的CPD方法，以进行动态图。我们的方法着重于学习网络的低维表示，并同时捕获这些学习潜在表示的不断发展的模式。具有不断发展的模式后，可以实现目标网络的预测。然后，我们可以通过利用权衡策略来比较预测和实际网络来检测变化点，该策略平衡了预测网络与从先前网络中提取的正常图形模式之间的重要性。对合成和现实数据集进行的密集实验显示了我们模型的有效性和优势。
Graph-based change point detection (CPD) play an irreplaceable role in discovering anomalous graphs in the time-varying network. While several techniques have been proposed to detect change points by identifying whether there is a significant difference between the target network and successive previous ones, they neglect the natural evolution of the network. In practice, real-world graphs such as social networks, traffic networks, and rating networks are constantly evolving over time. Considering this problem, we treat the problem as a prediction task and propose a novel CPD method for dynamic graphs via a latent evolution model. Our method focuses on learning the low-dimensional representations of networks and capturing the evolving patterns of these learned latent representations simultaneously. After having the evolving patterns, a prediction of the target network can be achieved. Then, we can detect the change points by comparing the prediction and the actual network by leveraging a trade-off strategy, which balances the importance between the prediction network and the normal graph pattern extracted from previous networks. Intensive experiments conducted on both synthetic and real-world datasets show the effectiveness and superiority of our model.
