NeTS: Small: Collaborative Research: Multi-Resolution Analysis & Measurement of Large-scale, Dynamic Networked Systems with Applications to Online Social Networks

许多大型网络系统，如在线社交网络（Online Social Networks, osn），通常被表示为带有各种节点或链接属性的带注释的图。这种表示通常是从通过测量获得的快照中得出的。这些图表示使研究人员能够使用图分析来表征这些系统的连通性。然而，捕获的大型网络系统的快照可能是扭曲的。此外，常用的图分析以间接的方式表征图的连通性，通常忽略图的动态。这个多学科的研究项目设计，开发和严格评估理论基础技术，以准确测量和适当表征大规模和动态网络系统的连接结构。更具体地说，该项目研究了从大型和不断发展的图中收集代表性样本的各种图采样技术。它还研究了如何将多尺度分析作为一种强大的技术来表征空间和时间上不同尺度的大型动态网络系统的连接结构的关键特征。所开发的技术将用于表征不同osn中友谊的基本属性和各种交互连接结构。该项目承诺确定主要驱动osn特定连接结构的结构特性和动态特性的潜在技术和社会因素。它将产生新的OSN友谊和互动模型，一个大型匿名数据集档案和OSN测量、模拟和分析的新工具。后者将纳入新开发的计算机科学和社会学课程，并将免费分发。

Many large-scale networked systems such as Online Social Networks (OSNs)are often represented as annotated graphs with various node or link attributes. Such a representation is usually derived from a snapshot that is obtained through measurements. These graph representations enable researchers to characterize the connectivity of these systems using graph analysis. However, captured snapshots of large networked systems are likely to be distorted. Furthermore, commonly-used graph analysis characterizes the connectivity of a graph in an indirect fashion and generally ignores graph dynamics.This multi-disciplinary research program designs, develops and rigorously evaluates theoretically grounded techniques to accurately measure and properly characterize the connectivity structure of large-scale and dynamic networked systems. More specifically, the project examines various graph sampling techniques for collecting representative samples from large and evolving graphs. It also investigates how multiscale analysis can be used as a powerful technique to characterize the key features of the connectivity structure of large dynamic networked systems at different scales in space and time. The developed techniques will be used to characterize fundamental properties of the friendship and various interaction connectivity structures in different OSNs.This project promises to identify the underlying technical and social factors that primarily drive the structural properties and dynamic nature of OSN-specific connectivity structures. It will produce new models for friendship and interactions in OSNs, a large archive of anonymized datasets and new tools for OSN measurement, simulation and analysis. The latter will be incorporated into newly-developed courses in Computer Science and Sociology, and will be freely distributed.