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.

翻译：本论文的主要目标是针对静态网络和单事件动态网络，开发新颖的图表示学习算法方法。在此方向上，我们聚焦于隐空间模型族，特别是隐距离模型，该模型天然地传递了同质性、传递性和平衡理论等重要网络特性。此外，本论文旨在创建结构感知的网络表示，从而实现网络结构的层次化表达、社区特征刻画、网络中极端模式的识别以及时序网络中影响动态的量化。关键的是，所提出的方法旨在定义统一的学习过程，消除了对启发式方法和多阶段处理（如后处理步骤）的需求。我们的目标是深入探索一种统一、全面且强大的网络嵌入方法，该方法既能表征网络结构，又能熟练处理图分析所提供的多样化任务。