Difference between revisions of "Wikipedia Graph Mining: Dynamic Structure of Collective Memory"
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| − | '''Wikipedia Graph Mining: Dynamic Structure of Collective Memory''' - scientific work related to Wikipedia quality published in 2017, written by Volodymyr Miz, Kirell Benzi, Benjamin Ricaud and Pierre Vandergheynst. | + | '''Wikipedia Graph Mining: Dynamic Structure of Collective Memory''' - scientific work related to [[Wikipedia quality]] published in 2017, written by [[Volodymyr Miz]], [[Kirell Benzi]], [[Benjamin Ricaud]] and [[Pierre Vandergheynst]]. |
== Overview == | == Overview == | ||
| − | 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, authors 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. Authors focus on data-streams with underlying graph structure and perform several large-scale experiments on the Wikipedia visitor activity data. Authors show that the presented model is scalable regarding time-series length and graph density, providing a distributed implementation of the proposed algorithm. Authors extract dynamical patterns of collective activity and demonstrate that they correspond to meaningful clusters of associated events, reflected in the Wikipedia articles. Authors also illustrate evolutionary dynamics of the graphs over time to highlight changing nature of visitors' interests. Finally, authors discuss clusters of events that model collective recall process and represent collective memories - common memories shared by a group of people. | + | 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, authors 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. Authors focus on data-streams with underlying graph structure and perform several large-scale experiments on the Wikipedia visitor activity data. Authors show that the presented model is scalable regarding time-series length and graph density, providing a distributed implementation of the proposed algorithm. Authors extract dynamical patterns of collective activity and demonstrate that they correspond to meaningful clusters of associated events, reflected in the Wikipedia articles. Authors also illustrate evolutionary dynamics of the graphs over time to highlight changing nature of visitors' interests. Finally, authors discuss clusters of events that model collective recall process and represent collective memories - common memories shared by a group of people. |
Revision as of 09:28, 18 May 2020
Wikipedia Graph Mining: Dynamic Structure of Collective Memory - scientific work related to Wikipedia quality published in 2017, written by Volodymyr Miz, Kirell Benzi, Benjamin Ricaud and Pierre Vandergheynst.
Overview
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, authors 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. Authors focus on data-streams with underlying graph structure and perform several large-scale experiments on the Wikipedia visitor activity data. Authors show that the presented model is scalable regarding time-series length and graph density, providing a distributed implementation of the proposed algorithm. Authors extract dynamical patterns of collective activity and demonstrate that they correspond to meaningful clusters of associated events, reflected in the Wikipedia articles. Authors also illustrate evolutionary dynamics of the graphs over time to highlight changing nature of visitors' interests. Finally, authors discuss clusters of events that model collective recall process and represent collective memories - common memories shared by a group of people.
