🚀 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
🚀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
We welcome full papers, position papers, and extended abstracts on Frontiers, Applications, Theory, Methods and Evaluation of Temporal Graphs. Find the full call for paper on our website
#KDD2025 #TemporalGraphs #MachineLearning
#KDD2025 #TemporalGraphs #MachineLearning
April 7, 2025 at 6:37 PM
We welcome full papers, position papers, and extended abstracts on Frontiers, Applications, Theory, Methods and Evaluation of Temporal Graphs. Find the full call for paper on our website
#KDD2025 #TemporalGraphs #MachineLearning
#KDD2025 #TemporalGraphs #MachineLearning
Hi!
This thursday, nov 21st, 11am EST, Rishabh Ranjan will present:
RELBENCH: A Benchmark for Deep Learning on Relational Databases (NeurIPS 2024 Datasets and Benchmarks Track)
🎈
Join on zoom (link on website)
arxiv.org/pdf/2407.20060
#graphml #machinelearning #temporalgraphs #neurips
This thursday, nov 21st, 11am EST, Rishabh Ranjan will present:
RELBENCH: A Benchmark for Deep Learning on Relational Databases (NeurIPS 2024 Datasets and Benchmarks Track)
🎈
Join on zoom (link on website)
arxiv.org/pdf/2407.20060
#graphml #machinelearning #temporalgraphs #neurips
Position: Relational Deep Learning - Graph Representation Learning on Relational Databases
Much of the world’s most valued data is stored in relational databases and data warehouses, where the data is organized into tables connected by primary-foreign key relations. However, building mac...
proceedings.mlr.press
November 18, 2024 at 3:35 PM
Hi!
This thursday, nov 21st, 11am EST, Rishabh Ranjan will present:
RELBENCH: A Benchmark for Deep Learning on Relational Databases (NeurIPS 2024 Datasets and Benchmarks Track)
🎈
Join on zoom (link on website)
arxiv.org/pdf/2407.20060
#graphml #machinelearning #temporalgraphs #neurips
This thursday, nov 21st, 11am EST, Rishabh Ranjan will present:
RELBENCH: A Benchmark for Deep Learning on Relational Databases (NeurIPS 2024 Datasets and Benchmarks Track)
🎈
Join on zoom (link on website)
arxiv.org/pdf/2407.20060
#graphml #machinelearning #temporalgraphs #neurips
Efficient Learning-based Graph Simulation for Temporal Graphs
Chenhao Xu, Dawei Cheng et al.
Paper
Details
#EfficientLearning #GraphSimulation #TemporalGraphs
Chenhao Xu, Dawei Cheng et al.
Paper
Details
#EfficientLearning #GraphSimulation #TemporalGraphs
October 9, 2025 at 4:01 PM
Efficient Learning-based Graph Simulation for Temporal Graphs
Chenhao Xu, Dawei Cheng et al.
Paper
Details
#EfficientLearning #GraphSimulation #TemporalGraphs
Chenhao Xu, Dawei Cheng et al.
Paper
Details
#EfficientLearning #GraphSimulation #TemporalGraphs