统一学习框架，让静态与单事件动态网络的结构特征可量化分析
Machine Learning for Static and Single-Event Dynamic Complex Network Analysis
- 基于潜在空间模型，融合同质性与传递性等网络特性
- 生成分层结构表示，支持社区识别与极端行为检测
- 无需后处理，适合复杂图分析任务的通用嵌入方法
本论文旨在开发针对静态与单事件动态网络的新型图表示学习算法。研究聚焦于潜在空间模型家族，特别是能自然表达同质性、传递性与平衡理论的潜在距离模型。目标是构建结构感知的网络表示，实现网络结构的分层表达、社区刻画、极端特征识别以及时间网络中的影响动态量化。关键在于，所提方法设计为统一的学习过程，避免启发式规则与多阶段后处理。致力于探索一种全面且强大的统一网络嵌入，既能表征网络结构，又能有效应对多样化的图分析任务。
原文摘要 · Abstract (English)
The primary objective of this thesis is to develop novel algorithmic approaches for Graph Representation Learning of static and single-event dynamic networks. In such a direction, we focus on the family of Latent Space Models, and more specifically on the Latent Distance Model which naturally conveys important network characteristics such as homophily, transitivity, and the balance theory. Furthermore, this thesis aims to create structural-aware network representations, which lead to hierarchical expressions of network structure, community characterization, the identification of extreme profiles in networks, and impact dynamics quantification in temporal networks. Crucially, the methods presented are designed to define unified learning processes, eliminating the need for heuristics and multi-stage processes like post-processing steps. Our aim is to delve into a journey towards unified network embeddings that are both comprehensive and powerful, capable of characterizing network structures and adeptly handling the diverse tasks that graph analysis offers.
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