Stefan Neumann
4 Papers
Stefan Neumann is an academic researcher. The author has contributed to research in topics: Computer science. The author has co-authored 1 publications.
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Papers
Sublinear-Time Opinion Estimation in the Friedkin--Johnsen Model
Stefan Neumann,Yinhao Dong,Pan Peng +2 more
- 25 Apr 2024
TL;DR: Sublinear-time opinion estimation in the Friedkin--Johnsen model allows for efficient approximation of opinions and relevant measures in online social networks with limited data access.
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Accepted Tutorials at The Web Conference 2022
Riccardo Tommasini,Senjuti Basu Roy,Xuan Wang,Hongwei Wang,Heng Ji,J.H. Han,Preslav Nakov,Giovanni Da San Martino,Mashiul Alam,Markus Schedl,Elisabeth Lex,Akash Bharadwaj,Graham Cormode,Milan Dojchinovski,Jan Forberg,Johannes Frey,Pieter Bonte,Marco Balduini,Matteo Belcao,Emanuele Della Valle,Junliang Yu,Hongzhi Yin,Tong Chen,Haochen Liu,Yiqi Wang,Wenqi Fan,Xiaorui Liu,Jamell Dacon,Lin Heng Lye,Jiliang Tang,Aristides Gionis,Stefan Neumann,Bruno Ordozgoiti,S. Razniewski,H. Arnaout,Shrestha Ghosh,Fabian M. Suchanek,Ling-Hua Wu,Yu Chen,Yunyao Li,Bang Liu,Filip Ilievski,Daniel Garijo,Hans Chalupsky,Pedro Szekely,Ilias Kanellos,Dimitris Sacharidis,Thanasis Vergoulis,Nurendra Choudhary,N. Rao,Karthik Subbian,Srinivasan H. Sengamedu,Chandana A. Reddy,Friedhelm Victor,Bernhard Haslhofer,George Katsogiannis-Meimarakis,Georgia Koutrika,Shengmin Jin,Danai Koutra,Reza Zafarani,Yulia Tsvetkov,Vidhisha Balachandran,Sachin Kumar,Xiangyu Zhao,Bo Chen,Huiwen Guo,Yejing Wang,Ruiming Tang,Yan Zhang,Wenjie Wang,Peng Wu,Fuli Feng,Xiangnan He +72 more
- 25 Apr 2022
TL;DR: This paper summarizes the content of the 20 tutorials that have been given at The Web Conference 2022: 85% of these tutorials are lecture style, and 15% are hands on.
OptiRefine: Densest subgraphs and maximum cuts with k refinements
Sijing Tu,Aleksa Stanković,Stefan Neumann,Aristides Gionis +3 more
TL;DR: OptiRefine framework optimizes initial solutions with k refinements, ensuring near-optimal solutions for densest subgraph and maximum cut problems while preserving similarity to initial solutions, with constant-factor approximation algorithms and scalable heuristics.
Modeling the Impact of Timeline Algorithms on Opinion Dynamics Using Low-rank Updates
Tianyi Zhou,Stefan Neumann,Kiran Garimella,Aristides Gionis +3 more
TL;DR: This work shows how the popular Friedkin--Johnsen opinion-formation model can be augmented based on aggregate information, extracted from timeline data, and presents a gradient descent-based algorithm that provably computes an $\varepsilon$-approximation of the model in near-linear time.