Wikipedia graph mining: dynamic structure of collective memory

TL;DR

This paper introduces a scalable, distributed graph-based model inspired by Hebbian learning to extract and analyze collective events and memories from Wikipedia visitor activity data, revealing dynamic patterns and evolving interests.

Contribution

It presents a novel distributed event extraction model for graph-structured data, specifically applied to Wikipedia, capturing collective activity patterns and their evolution over time.

Findings

The model effectively identifies meaningful event clusters.

It demonstrates scalability with data size and graph complexity.

The analysis reveals evolving collective interests and shared memories.

Abstract

Wikipedia is the biggest encyclopedia ever created and the fifth most visited website in the world. Tens of millions of people surf it every day, seeking answers to various questions. Collective user activity on its pages leaves publicly available footprints of human behavior, making Wikipedia an excellent source for analysis of collective behavior. In this work, we propose a distributed graph-based event extraction model, inspired by the Hebbian learning theory. The model exploits collective effect of the dynamics to discover events. We focus on data-streams with underlying graph structure and perform several large-scale experiments on the Wikipedia visitor activity data. We show that the presented model is scalable regarding time-series length and graph density, providing a distributed implementation of the proposed algorithm. We extract dynamical patterns of collective activity and demonstrate that they correspond to meaningful clusters of associated events, reflected in the Wikipedia articles. We also illustrate evolutionary dynamics of the graphs over time to highlight changing nature of visitors' interests. Finally, we discuss clusters of events that model collective recall process and represent collective memories - common memories shared by a group of people.