Networks are a fundamental and flexible way of representing various complex systems. Many domains such as communication, citation, procurement, biology, social media, and transportation can be modeled as a set of entities and relationships among them. Temporal networks are a specialization of general networks where temporal evolution of the system is as important to understand as the structure of entities and relationships.
This code discovers Independent Temporal Motif (ITeM) in a temporal network. It takes a temporal graph of the format
source,edge_type,destination,time
and generates various distributions using ITeM
Please contact [email protected] for any question.
#clone TAGBuilder and install it in <HOME> dir
git clone https://github.com/temporal-graphs/TAGBuilder.git
cd TAGBuilder/code/STMBase
mvn clean install
cd TAGBuilder/code/TAGBuilder
mvn clean install
cd <HOME>
git clone https://github.com/temporal-graphs/STM.git
cd STM
mvn clean package
It generates an uber-jar in the target directory which can be used to generate the ITeM distributions
java -cp target/uber-STM-1.4-SNAPSHOT.jar gov.pnnl.stm.algorithms.STM_NodeArrivalRateMultiType -input_file="input.csv" -separator="," -sampling=false -valid_etypes=1 -delta_limit=false -k_top=4 -max_cores=1 -base_out_dir="./item-output/"
where input.csv has following format
1,0,2,1001
1,0,3,1002
1,0,4,1002
2,0,5,1003
It generates multiple internal files for different temporal properties. Follwoing script reads them in and generate "graph embeddings" and "node embeddings"
python STMGetEmbedding.py './item-output/' './emb/'
If you find this useful, please cite following publication
@article{purohit2022item,
title={ITeM: Independent temporal motifs to summarize and compare temporal networks},
author={Purohit, Sumit and Chin, George and Holder, Lawrence B},
journal={Intelligent Data Analysis},
volume={26},
number={4},
pages={1071--1096},
year={2022},
publisher={IOS Press}
}
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