EAGER: Multi-Stream Graph Mining

图，或节点和链接的网络，已被证明在许多应用程序中发现模式是有效的，其中数据是从多个相连的源生成的。在复杂的现实应用程序中，图形通常是随着时间的推移收集的，称为图形流数据。现有的基于图的知识发现方法在计算上具有挑战性，特别是在分析大量的图流时。此外，通常不能通过挖掘单个图流来更好地理解内在模式。该项目旨在研究一种新的能够在多图流中发现可伸缩知识的方法，称为多流图挖掘。这种方法是新颖的，在如何从多个数据流中发现知识方面具有潜在的变革性。该项目将产生重大影响，提供高效和有效的工具来检测不同数据中的模式，这些模式可以在各种可获得大量动态数据的领域产生新的发现，包括网络安全和社交媒体。本项目将研究一组从多个图流中实时挖掘模式的数据挖掘方法。这些方法执行不同数量和类型的个体流预处理，以便有效地减少数据的大小和到达率。这些方法是：(1)对数据流进行采样，(2)基于已知模式对数据流进行压缩，(3)首先对单个流进行挖掘，然后利用挖掘出的模式进行多流融合。这些方法将使用人工和真实世界的多流图形数据进行评估，结果将通过软件发布和出版物传播。这项研究将增进我们对如何有效地处理以图表示的多个数据流以便实时学习结构模式的知识和理解。在该项目下开发的方法代表了新级别的可扩展性，这对于应对当今的大数据挑战以及用户快速发现可操作情报的需求是必要的。

Graphs, or networks of nodes and links, have proven to be effective for discovering patterns in many applications where data are generated from multiple and connected sources. In complex real-world applications, graphs are typically collected over time, known as graph stream data. Existing graph-based approaches for knowledge discovery are computationally challenging, particularly when analyzing large amounts of graph streams. In addition, a better understanding of intrinsic patterns typically cannot be obtained through mining a single graph stream. This project aims to investigate a new approach capable of scalable knowledge discovery in multiple graph streams, called multi-stream graph mining. The approach is novel and potentially transformative in how knowledge is discovered from multiple data streams. The project will have significant impact by providing efficient and effective tools for detecting patterns in heterogeneous data that can lead to new discoveries in a variety of domains where large amounts of dynamic data are available, including cyber-security and social media. This project will investigate a group of data mining approaches for mining patterns from multiple graph streams in real-time. The methods perform different amounts and types of individual-stream pre-processing in order to effectively reduce the size and arrival-rate of the data. These methods are: (1) sampling the data streams, (2) compressing the data streams based on known patterns, (3) mining individual streams first and then utilizing the mined patterns for performing multiple stream fusion. The methods will be evaluated using both artificial and real-world multi-stream graph data, and results will be disseminated via software releases and publications. This research will advance our knowledge and understanding of how to efficiently process multiple data streams represented as graphs in order to learn structural patterns in real time. The methods developed under this project represent a new level of scalability that is necessary to address today's big data challenges, as well as users' needs to quickly discover actionable intelligence.