# Exploring the Evolution of Node Neighborhoods in Dynamic Networks

## TL;DR

This paper introduces a novel method for analyzing the evolution of node neighborhoods in dynamic networks by detecting neighborhood events and applying pattern mining, revealing behavioral trends and node classifications.

## Contribution

It proposes a new approach to characterize node roles in dynamic networks through neighborhood event detection and analysis, enhancing understanding of network evolution.

## Key findings

- Identification of two main node behavior groups: active and stable.
- Demonstration on real-world networks: DBLP, LastFM, Enron.
- Discovery of behavioral trends and node classifications.

## Abstract

Dynamic Networks are a popular way of modeling and studying the behavior of evolving systems. However, their analysis constitutes a relatively recent subfield of Network Science, and the number of available tools is consequently much smaller than for static networks. In this work, we propose a method specifically designed to take advantage of the longitudinal nature of dynamic networks. It characterizes each individual node by studying the evolution of its direct neighborhood, based on the assumption that the way this neighborhood changes reflects the role and position of the node in the whole network. For this purpose, we define the concept of \textit{neighborhood event}, which corresponds to the various transformations such groups of nodes can undergo, and describe an algorithm for detecting such events. We demonstrate the interest of our method on three real-world networks: DBLP, LastFM and Enron. We apply frequent pattern mining to extract meaningful information from temporal sequences of neighborhood events. This results in the identification of behavioral trends emerging in the whole network, as well as the individual characterization of specific nodes. We also perform a cluster analysis, which reveals that, in all three networks, one can distinguish two types of nodes exhibiting different behaviors: a very small group of active nodes, whose neighborhood undergo diverse and frequent events, and a very large group of stable nodes.