EAGER: Multi-Stream Graph Mining

图，或节点和链接的网络，已被证明可以有效地发现许多应用程序中的模式，其中数据是从多个连接的源生成的。在复杂的现实世界应用中，图形通常随时间收集，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，特别是在分析大量图流时。此外，通过挖掘单个图流通常无法更好地理解内在模式。本项目旨在研究一种能够在多个图流中进行可扩展知识发现的新方法，称为多流图挖掘。该方法是新颖的，并且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，用于检测异构数据中的模式，从而在包括网络安全和社交媒体在内的大量动态数据可用的各种领域中产生新的发现。本项目将研究一组数据挖掘方法，用于实时地从多个图流中挖掘模式。这些方法执行不同数量和类型的单独流预处理，以便有效地减小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）首先挖掘各个流，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。这项研究将促进我们的知识和理解，如何有效地处理多个数据流表示为图形，以学习结构模式在真实的时间。该项目开发的方法代表了一个新的可扩展性水平，这是解决当今大数据挑战所必需的，也是用户快速发现可操作情报的需求。

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.