Graph embedding: time and space

图嵌入：时间和空间

• 批准号: 2438032
• 金额: --
• 依托单位: University of Bristol
• 依托单位国家: 英国
• 项目类别: Studentship
• 财政年份: 2020
• 资助国家: 英国
• 起止时间: 2020 至 无数据
• 项目状态: 未结题
• 来源: https://gtr.ukri.org/projects?ref=studentship-2438032
• 关键词: Graph; embedding; time; space

Advances in the analysis of large networks have benefitted very many fields, from cyber-security in the identification of intruders by modelling when two IP addresses should normally communicate, to neuroscience in which differences in brain connectivity could be an indication of schizophrenia. Analyses like these often involve a first step where the network data is represented as a lower-dimensional vector space known as an embedding, in which nodes that behave similarly are ""close"".This project will focus on producing embeddings that correctly represent large dynamic networks. This involves taking ""snapshots"" of the dynamic network over time and then calculating an embedding representation of each snapshot such that communities of nodes are positioned correctly over time.A simple method of modelling a network with community structure is the stochastic block model. This is an example of a latent position model which generates random networks where nodes of the same community will behave similarly and share latent positions. From this, we can consider a dynamic stochastic block model where communities and latent positions will move (or stay static) over time. The goal of this project is then to discover a method that can perfectly recover the community membership of each node in a dynamic stochastic block model as the network becomes asymptotically large. If achieved, this will be an incredibly important result with simple implications for the correct analysis to perform across many application domains.

大型网络分析的进展使许多领域受益，从网络安全中识别入侵者的建模，当两个IP地址正常通信时，神经科学中大脑连接的差异可能是精神分裂症的一个迹象。像这样的分析通常涉及第一步，其中网络数据表示为称为嵌入的低维向量空间，其中行为相似的节点是“接近”的。该项目将专注于产生正确表示大型动态网络的嵌入。这涉及到随着时间的推移拍摄动态网络的“快照”，然后计算每个快照的嵌入表示，使得节点社区随着时间的推移正确定位。这是一个潜在位置模型的例子，它生成随机网络，其中同一社区的节点将表现相似并共享潜在位置。由此，我们可以考虑一个动态随机块模型，其中社区和潜在位置将随着时间的推移而移动（或保持静态）。这个项目的目标是发现一种方法，可以完美地恢复社区成员的每个节点在一个动态随机块模型的网络变得渐近大。如果实现了，这将是一个非常重要的结果，对跨许多应用程序域执行正确的分析具有简单的意义。