Temporal Pattern Mining from Evolving Networks

TL;DR

This paper explores how temporal pattern mining techniques can analyze evolving networks by using snapshots to detect and characterize their dynamic changes without assuming specific evolutionary models.

Contribution

It introduces a framework for applying temporal pattern mining to evolving networks, focusing on extracting knowledge from network snapshots without prior assumptions.

Findings

Effective detection of network changes over time

Characterization of temporal dynamics in networks

Potential for uncovering hidden patterns

Abstract

Recently, evolving networks are becoming a suitable form to model many real-world complex systems, due to their peculiarities to represent the systems and their constituting entities, the interactions between the entities and the time-variability of their structure and properties. Designing computational models able to analyze evolving networks becomes relevant in many applications. The goal of this research project is to evaluate the possible contribution of temporal pattern mining techniques in the analysis of evolving networks. In particular, we aim at exploiting available snapshots for the recognition of valuable and potentially useful knowledge about the temporal dynamics exhibited by the network over the time, without making any prior assumption about the underlying evolutionary schema. Pattern-based approaches of temporal pattern mining can be exploited to detect and characterize changes exhibited by a network over the time, starting from observed snapshots.