CGV: Small: Making Sense out of Large Graphs - Bridging HCI with Data Mining

这个研究项目的目标是帮助人们理解大型图，从社交网络到网络流量。该方法包括将两个互补的领域结合起来，这两个领域在历史上几乎没有互动-数据挖掘和人机交互-以开发交互式算法和界面，帮助用户从具有数十万个节点和边缘的图形中获得见解。 该项目的目标是开发混合初始机器学习，可视化和交互技术，其中计算机做他们最擅长的事情（筛选大量数据并发现异常值），而人类做他们最擅长的事情（识别模式，测试假设和诱导模式）。这项研究解决了两类任务：第一，注意力路由-使用机器学习将分析师的注意力引导到不符合正常行为的有趣节点或子图。第二，意义建构--帮助分析师建立图形特定区域或方面的深入表示和心理模型。对这些工具的评估将涉及受控实验室研究以及长期的实地部署。随着大型图表出现在许多环境中--国家安全、入侵检测、商业智能（推荐系统、欺诈检测）、生物学（基因调控）和学术界（科学文献）--理解图表的新工具的潜在好处是深远的。项目成果，包括开放源码软件和附加说明的数据集，将通过项目网站（http：//kittur.org/large_graphs.html）传播，并纳入教育活动。

The goal of this research project is to help people make sense of large graphs, ranging from social networks to network traffic. The approach consists of combining two complementary fields that have historically had little interaction -- data mining and human-computer interaction -- to develop interactive algorithms and interfaces that help users gain insights from graphs with hundreds of thousands of nodes and edges. The goal of the project is to develop mixed-initative machine learning, visualization, and interaction techniques in which computers do what they are best at (sifting through huge volumes of data and spotting outliers) while humans do what they are best at (recognizing patterns, testing hypotheses, and inducing schemas). This research addresses two classes of tasks: first, attention routing -- using machine learning to direct an analyst's attention to interesting nodes or subgraphs that do not conform to normal behavior. Second, sensemaking -- helping analysts build in-depth representations and mental models of a specific areas or aspects of a graph. Evaluation of the tools will involve both controlled laboratory studies as well as long-term field deployments.As large graphs appear in many settings -- national security, intrusion detection, business intelligence (recommendation systems, fraud detection), biology (gene regulation), and academia (scientific literature) -- the potential benefits of new tools for making sense of graphs is far reaching. Project results, including open-source software and annotated data sets, will be disseminated via the project web site (http://kittur.org/large_graphs.html) and incorporated into educational activities.