在不断演化的网络中挖掘持久活动
Mining Persistent Activity in Continually Evolving Networks
摘要：
频繁模式挖掘是一个关键的研究领域，它可以深入了解不断发展的网络（如社交网络或道路网络）的结构和动态。然而，不仅网络会进化，而且它进化的方式本身也会进化。因此，除了知道模式的频率之外，还知道它们发生的时间和频率-即，它们的持久性可以增加我们对进化网络的理解。在这项工作中，我们提出了在不断发展的网络中持续存在的挖掘活动的问题-即，重复和持续发生的活动。我们扩展了时间图案的概念，以捕获特定节点之间的活动，我们称之为活动片段，这是重复发生的边缘更新的小序列。我们提出了公理和性质，持久性的措施应该满足，并开发这样的持久性措施。我们还提出了PENminer，一个有效的框架，挖掘活动片段的持久性在不断发展的网络，并设计了离线和流式算法。我们将PENminer应用于许多真实的、大规模的不断发展的网络和边缘流，并发现了在很长一段时间内令人惊讶的规律性活动，但仅通过聚合计数就无法发现这些活动，以及由于缺乏持久性而暴露出来的活动爆发。我们与PENminer的研究结果包括纽约市的出租车交通在飓风桑迪期间持续存在的社区，新自行车站的开放，社交网络用户的特征等等。此外，我们使用PENminer来识别多个网络中的异常，在AUC中识别细微异常的性能优于基线9.8-48%。
英文摘要：
Frequent pattern mining is a key area of study that gives insights into the structure and dynamics of evolving networks, such as social or road networks. However, not only does a network evolve, but often the way that it evolves, itself evolves. Thus, knowing, in addition to patterns' frequencies, for how long and how regularly they have occurred---i.e., their persistence---can add to our understanding of evolving networks. In this work, we propose the problem of mining activity that persists through time in continually evolving networks---i.e., activity that repeatedly and consistently occurs. We extend the notion of temporal motifs to capture activity among specific nodes, in what we call activity snippets, which are small sequences of edge-updates that reoccur. We propose axioms and properties that a measure of persistence should satisfy, and develop such a persistence measure. We also propose PENminer, an efficient framework for mining activity snippets' Persistence in Evolving Networks, and design both offline and streaming algorithms. We apply PENminer to numerous real, large-scale evolving networks and edge streams, and find activity that is surprisingly regular over a long period of time, but too infrequent to be discovered by aggregate count alone, and bursts of activity exposed by their lack of persistence. Our findings with PENminer include neighborhoods in NYC where taxi traffic persisted through Hurricane Sandy, the opening of new bike-stations, characteristics of social network users, and more. Moreover, we use PENminer towards identifying anomalies in multiple networks, outperforming baselines at identifying subtle anomalies by 9.8-48% in AUC.
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。
