A supervised approach to time scale detection in dynamic networks
A supervised approach to time scale detection in dynamic networks
复制标题
动态网络中时间尺度检测的监督方法
DOI：
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发表时间：
2017
期刊：
影响因子：
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通讯作者：
R. Caceres
中科院分区：
文献类型：
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作者：
Benjamin Fish;R. Caceres
对于任何形成动态网络的带有时间戳的边缘流，一个重要的选择是分析人员用来存放数据的聚合粒度。选择这样一个窗口的数据通常是手工完成的，或者留给收集数据的技术。然而，这种选择会对动态网络的特性产生很大的影响。这就是时间尺度检测问题。在以前的工作中，这个问题通常是用启发式的无监督任务来解决的。作为一个无监督问题，很难衡量给定算法的性能。此外，我们表明窗口的质量取决于分析人员在窗口后想要在网络上执行的任务。因此，时间尺度检测问题不应该独立于网络分析的其余部分来处理。
For any stream of time-stamped edges that form a dynamic network, an important choice is the aggregation granularity that an analyst uses to bin the data. Picking such a windowing of the data is often done by hand, or left up to the technology that is collecting the data. However, the choice can make a big difference in the properties of the dynamic network. This is the time scale detection problem. In previous work, this problem is often solved with a heuristic as an unsupervised task. As an unsupervised problem, it is difficult to measure how well a given algorithm performs. In addition, we show that the quality of the windowing is dependent on which task an analyst wants to perform on the network after windowing. Therefore the time scale detection problem should not be handled independently from the rest of the analysis of the network.
We introduce a framework that tackles both of these issues: By measuring the performance of the time scale detection algorithm based on how well a given task is accomplished on the resulting network, we are for the first time able to directly compare different time scale detection algorithms to each other. Using this framework, we introduce time scale detection algorithms that take a supervised approach: they leverage ground truth on training data to find a good windowing of the test data. We compare the supervised approach to previous approaches and several baselines on real data.