Theory and Application of Temporal Network Embedding

时态网络嵌入理论与应用

• 批准号: 2204936
• 负责人: Naoki Masuda
• 金额: $ 30万
• 依托单位: SUNY at Buffalo
• 依托单位国家: 美国
• 项目类别: Standard Grant
• 财政年份: 2022
• 资助国家: 美国
• 起止时间: 2022-09-01 至 2025-08-31
• 项目状态: 未结题
• 来源: https://www.nsf.gov/awardsearch/showAward?AWD_ID=2204936&HistoricalAwards=false
• 关键词: Theory; Application; Temporal; Network; Embedding

Many complex systems in the real world can be modeled as networks. In fact, many networks vary over time. For example, contact networks change from one shape to another as people move around to meet different people. Friendship networks also vary over time on a longer timescale. Such temporal (i.e., time-varying) network data have been increasingly available, and mathematically founded methods that can efficiently summarize complex temporal network data to help enhance intuitive understanding of the data are desirable. The Principal Investigator (PI) will develop methods to map temporal network data to trajectories in a space. Specifically, the methods will enable representation of the network at a given time point succinctly as a point on the trajectory. This is a drastic reduction, but in this manner aims to capture gross properties of the data and potentially use them for data mining tasks such as visualization, anomaly detection, and discovery of hidden periodicity. The PI will then build mathematical foundations of the proposed methods and apply them to empirical data. The proposed methods are expected to find applications in online social network services, financial transactions, bibliographic citation data, neuroimaging data, and climate temporal networks, to name a few. Furthermore, the project outcomes are expected to encourage researchers in data science and engineering to work on various algorithms related to network embedding (e.g., use of deep learning architecture). In this manner, the project relates to multiple research communities and industries. The methods to be developed in this project are temporal network embedding (TNE) methods. In contrast with most TNE methods available to date, in which one embeds nodes into a latent space, the class of TNE methods the PI will pursue is a mapping from the space of networks to a low-dimensional latent space. A fundamental challenge to TNE is that empirical data usually come in the form of a set of time-stamped events between pairs of nodes, which would generate an extremely sparse network at any given time, hampering sensible network analyses. To overcome this situation, the PI will combine the modeling framework called tie-decay temporal networks with a Nystrom family of general-purpose dimension reduction methods to establish a family of TNE methods. This particular combination of techniques will allow the PI to narrow down the methodological choice, such as which dimension reduction methods, network distance measures, and tie-decay functions should be used, as well as to facilitate efficient computations and mathematical investigations. The PI will then develop mathematical foundations of the proposed methods such as continuity, responses to Markovian inputs, and sensitivity to perturbation in the input data. Finally, the PI will showcase the methods by applying them to social and financial empirical data.This award reflects NSF's statutory mission and has been deemed worthy of support through evaluation using the Foundation's intellectual merit and broader impacts review criteria.

现实世界中的许多复杂系统都可以建模为网络。实际上，随着时间的流逝，许多网络都会有所不同。例如，随着人们四处走动以结识不同的人，接触网络会从一种形状变为另一种形状。随着时间的流逝，友谊网络在更长的时间范围内也有所不同。这种时间（即时变）网络数据已经越来越多，并且可以有效地汇总复杂的时间网络数据以帮助增强对数据的直觉理解的数学创建的方法。主要研究者（PI）将开发将时间网络数据映射到空间中的轨迹的方法。具体而言，这些方法将在给定时间点上简洁地表示网络作为轨迹上的点。这是一个急剧的减少，但是以这种方式旨在捕获数据的总特性，并有可能将其用于数据挖掘任务，例如可视化，异常检测和发现隐藏周期性。然后，PI将建立所提出方法的数学基础，并将其应用于经验数据。预计所提出的方法将在线社交网络服务，财务交易，书目引文数据，神经影像学数据和气候时间网络中找到应用程序。此外，预计该项目成果将鼓励数据科学和工程研究人员从事与网络嵌入有关的各种算法（例如，使用深度学习体系结构）。通过这种方式，该项目与多个研究社区和行业有关。该项目中要开发的方法是时间网络嵌入（TNE）方法。与迄今为止可用的大多数TNE方法相比，将节点嵌入潜在空间中，PI将追求的TNE方法是从网络空间到低维的潜在空间的映射。对TNE的一个基本挑战是，经验数据通常以一组时间stamp的事件形式出现，这对节点对，这将在任何给定时间产生极为稀疏的网络，从而阻碍了明智的网络分析。为了克服这种情况，PI将将称为Tin-Decay Perimal Networks的建模框架与尼斯特罗姆（Nystrom）家族的通用尺寸减小方法的nystrom家族相结合，以建立一种TNE方法家族。这种特殊的技术组合将使PI能够缩小方法论选择，例如，应使用降低尺寸的方法，网络距离测量和扎赛函数，并促进有效的计算和数学研究。然后，PI将开发提出的方法的数学基础，例如连续性，对马尔可夫输入的响应以及对输入数据中扰动的敏感性。最后，PI将通过将其应用于社会和财务经验数据来展示这些方法。该奖项反映了NSF的法定任务，并被认为是值得通过基金会的知识分子优点和更广泛的影响审查标准通过评估来支持的。

项目成果

Embedding and Trajectories of Temporal Networks

• DOI: 10.1109/access.2023.3268030
• 发表时间: 2022-08
• 期刊: IEEE Access
• 影响因子: 3.9
• 作者: Chanon Thongprayoon;L. Livi;N. Masuda