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

2025-12-19

AI Summary

This paper addresses graph representation learning for both static and single-event dynamic networks. Methodologically, it introduces a unified structural-aware embedding framework grounded in latent distance modeling, jointly optimizing homophily, transitivity, and balance within an end-to-end paradigm—thereby eliminating heuristic design and multi-stage pipelines. Notably, it is the first to extend latent distance modeling to single-event dynamic settings, enabling extreme node identification and quantitative assessment of influence dynamics. The key contributions are: (1) a hierarchical, interpretable structural-aware representation; (2) seamless unification of embedding learning across static and dynamic networks; and (3) state-of-the-art performance on community detection, anomaly detection, and temporal influence evaluation—significantly outperforming multi-stage baselines.

Technology Category

• Data Mining & Knowledge Management: Graph Mining, Social Network Analysis & Community
• Machine Learning: Graph-based Machine Learning
• Reasoning under Uncertainty: Uncertainty Representations

Application Category

• Graph Algorithms and Modeling for the Web: Graph embeddings and representation learning for Web-related graphs
• Social Networks and Social Media: Influence propagation, information diffusion, and the prediction on networks
• Web Mining and Content Analysis: Bridging structured and unstructured data

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.

Problem

• Develop novel algorithmic approaches for Graph Representation Learning
• Create structural-aware network representations for hierarchical expressions
• Define unified learning processes eliminating heuristics and multi-stage steps

Innovation

• Latent Distance Model for network characteristics
• Structural-aware representations for hierarchical expressions
• Unified learning processes eliminating heuristics