Wikipedia Graph Mining: Dynamic structure of collective memory

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. Collective memory is an interesting social phenomenon of human behavior. Studying this concept is a way to enhance our understanding of a common view of events in social groups and identify the events that influence collective remembering of the past. Collective memory hypothesis influenced a range of studies in sociology, psychology, cognitive sciences, and, only recently, in machine learning. We applied a data mining approach to studying collective memories.

In this talk, I will demonstrate a new method for collective memory retrieval and show how interests of Wikipedia visitors evolve over time. To reveal memory patterns, we analyze the seven months logs of user activity on Wikipedia and its Web network structure (5K+ hours, 100K+ active pages, 6.5M+ links). We use the Hopfield network model as an artificial memory abstraction to build a macroscopic collective memory model. Each pattern in the Hopfield network is a cluster of Wikipedia pages sharing a common topic and describing an event that triggered human curiosity during a finite period of time.

Volodymyr Miz

Volodymyr Miz is an Electrical Engineering PhD student at the EPFL (Swiss Federal Institute of Technology). His interests are related to graphs and networks analysis, time-series processing and data mining. His research focuses primarily on time-varying data with an underlying network structure. After obtaining BS and MS degrees in Computer Engineering from National University of Radio Electronics (Kharkov, Ukraine) in 2013, he worked for four years as a software engineer in a telecommunication company EchoStar and then joined EPFL as a PhD student.EPFL