发现交互网络中的主题和时间相干事件
Discovering topically- and temporally-coherent events in interaction networks
摘要：
随着电子邮件、twitter和消息应用程序等在线通信平台的使用越来越多，我们面临着越来越多的数据，这些数据结合了内容（说了什么）、时间（什么时候）和用户（谁）信息。一个重要的计算挑战是分析这些数据，发现有意义的模式，并了解正在发生的事情。我们考虑的问题，挖掘在线通信数据，并找到前k个时间事件。我们将时间事件定义为在相对较短的时间跨度内频繁讨论的连贯主题，而事件的信息流则尊重底层网络结构。我们构建我们的模型检测时间事件分两步。我们首先介绍了交互元图的概念，它连接相关的交互。使用这个概念，我们定义一个时间事件是一个子集的相互作用，（i）是局部和时间接近，（ii）对应于一个树，捕捉信息流。找到最好的时间事件导致预算版本的奖品收集Steiner树（PCST）的问题，我们解决使用三种不同的方法：贪婪的方法，动态规划算法，和适应现有的近似算法。在一组候选事件中寻找前k个事件的问题映射为最大集合覆盖问题，因此，用贪婪算法求解。我们比较和分析我们的算法在合成和真实的数据集，如Twitter和电子邮件通信。结果表明，我们的方法能够检测有意义的时间事件。
英文摘要：
With the increasing use of online communication platforms, such as email, twitter, and messaging applications, we are faced with a growing amount of data that combine content (what is said), time (when), and user (by whom) information. An important computational challenge is to analyze these data, discover meaningful patterns, and understand what is happening. We consider the problem of mining online communication data and finding top-k temporal events. We define a temporal event to be a coherent topic that is discussed frequently, in a relatively short time span, while the information ow of the event respects the underlying network structure. We construct our model for detecting temporal events in two steps. We first introduce the notion of interaction meta-graph, which connects associated interactions. Using this notion, we define a temporal event to be a subset of interactions that (i) are topically and temporally close and (ii) correspond to a tree that captures the information ow. Finding the best temporal event leads to budget version of the prize-collecting Steiner-tree (PCST) problem, which we solve using three different methods: a greedy approach, a dynamic-programming algorithm, and an adaptation to an existing approximation algorithm. The problem of finding the top- k events among a set of candidate events maps to maximum set-cover problem, and thus, solved by greedy. We compare and analyze our algorithms in both synthetic and real datasets, such as twitter and email communication. The results show that our methods are able to detect meaningful temporal events.
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。
