@lutzoettershagen.bsky.social
Assistant Professor at the Department of Computer Science, University of Liverpool.
https://lutzoe.github.io/
https://lutzoe.github.io/
🚀 TGLib is now on PyPI!
Our open-source library for efficient temporal graph analysis is now easy to install (Linux):
pip install temporalgraphlib
📄 Paper: arxiv.org/abs/2209.12587
💻 Code: gitlab.com/tgpublic/tglib
#TGLib #TemporalGraphs #Python #OpenSource #NetworkScience
Our open-source library for efficient temporal graph analysis is now easy to install (Linux):
pip install temporalgraphlib
📄 Paper: arxiv.org/abs/2209.12587
💻 Code: gitlab.com/tgpublic/tglib
#TGLib #TemporalGraphs #Python #OpenSource #NetworkScience
TGLib: An Open-Source Library for Temporal Graph Analysis
We initiate an open-source library for the efficient analysis of temporal graphs. We consider one of the standard models of dynamic networks in which each edge has a discrete timestamp and transition ...
arxiv.org
August 1, 2025 at 3:55 PM
🚀 TGLib is now on PyPI!
Our open-source library for efficient temporal graph analysis is now easy to install (Linux):
pip install temporalgraphlib
📄 Paper: arxiv.org/abs/2209.12587
💻 Code: gitlab.com/tgpublic/tglib
#TGLib #TemporalGraphs #Python #OpenSource #NetworkScience
Our open-source library for efficient temporal graph analysis is now easy to install (Linux):
pip install temporalgraphlib
📄 Paper: arxiv.org/abs/2209.12587
💻 Code: gitlab.com/tgpublic/tglib
#TGLib #TemporalGraphs #Python #OpenSource #NetworkScience
🚀 In our WebConf’24 paper, we tackle a new twist on densest subgraphs: finding diverse communities via edge-color constraints! 🌈🔍
We prove hardness and give a fast approximation for large sparse graphs. 📈
With H. Wang & A. Gionis
arxiv.org/abs/2402.09124
#WebConf24 #GraphMining #Diversity
We prove hardness and give a fast approximation for large sparse graphs. 📈
With H. Wang & A. Gionis
arxiv.org/abs/2402.09124
#WebConf24 #GraphMining #Diversity
Finding Densest Subgraphs with Edge-Color Constraints
We consider a variant of the densest subgraph problem in networks with single or multiple edge attributes. For example, in a social network, the edge attributes may describe the type of relationship b...
arxiv.org
July 12, 2025 at 4:14 AM
🚀 In our WebConf’24 paper, we tackle a new twist on densest subgraphs: finding diverse communities via edge-color constraints! 🌈🔍
We prove hardness and give a fast approximation for large sparse graphs. 📈
With H. Wang & A. Gionis
arxiv.org/abs/2402.09124
#WebConf24 #GraphMining #Diversity
We prove hardness and give a fast approximation for large sparse graphs. 📈
With H. Wang & A. Gionis
arxiv.org/abs/2402.09124
#WebConf24 #GraphMining #Diversity
🚀Our WSDM'25 paper introduces a new edge-based framework for decomposing temporal networks.
⚡️Scales to 100M+ edges, reveals structures in dynamic data—eg, misinformation patterns on Twitter
📄 arxiv.org/abs/2309.11843
With A.Konstantinidis & G.Italiano
#temporalgraphs #graphmining #misinformation
⚡️Scales to 100M+ edges, reveals structures in dynamic data—eg, misinformation patterns on Twitter
📄 arxiv.org/abs/2309.11843
With A.Konstantinidis & G.Italiano
#temporalgraphs #graphmining #misinformation
An Edge-Based Decomposition Framework for Temporal Networks
A temporal network is a dynamic graph where every edge is assigned an integer time label that indicates at which discrete time step the edge is available. We consider the problem of hierarchically dec...
arxiv.org
July 12, 2025 at 3:50 AM
🚀Our WSDM'25 paper introduces a new edge-based framework for decomposing temporal networks.
⚡️Scales to 100M+ edges, reveals structures in dynamic data—eg, misinformation patterns on Twitter
📄 arxiv.org/abs/2309.11843
With A.Konstantinidis & G.Italiano
#temporalgraphs #graphmining #misinformation
⚡️Scales to 100M+ edges, reveals structures in dynamic data—eg, misinformation patterns on Twitter
📄 arxiv.org/abs/2309.11843
With A.Konstantinidis & G.Italiano
#temporalgraphs #graphmining #misinformation