Machine Learning for Static and Single-Event Dynamic Complex Network Analysis

TL;DR

This thesis develops unified, structural-aware graph embedding algorithms for static and dynamic networks, leveraging Latent Distance Models to capture key network properties and facilitate diverse analysis tasks.

Contribution

It introduces novel algorithmic approaches for graph representation learning that unify multiple tasks without heuristics, focusing on Latent Distance Models for static and single-event dynamic networks.

Findings

Effective network characterizations achieved

Unified learning processes eliminate heuristics

Hierarchical and impact-aware embeddings created

Abstract

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.