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Title: Temporal Graph Generation Based on a Distribution of Temporal Motifs

Abstract

Generating a synthetic graph that is similar to a given real-world graph is a critical requirement for privacy preservation and benchmarking purposes. Various generative models attempt to generate static graphs similar to real-world graphs. However, generation of temporal graphs is still an open research area. We present a temporal-motif based approach to generate synthetic temporal graph datasets and results from three real-world use cases.

Authors:
ORCiD logo [1];  [2];  [1]
  1. BATTELLE (PACIFIC NW LAB)
  2. Washington State University
Publication Date:
Research Org.:
Pacific Northwest National Lab. (PNNL), Richland, WA (United States)
Sponsoring Org.:
USDOE
OSTI Identifier:
1507763
Report Number(s):
PNNL-SA-134797
DOE Contract Number:  
AC05-76RL01830
Resource Type:
Conference
Resource Relation:
Conference: 14TH INTERNATIONAL WORKSHOP ON MINING AND LEARNING WITH GRAPHS (MLG 2018), August 20, 2018, London, United Kingdom
Country of Publication:
United States
Language:
English

Citation Formats

Purohit, Sumit, Holder, Larry, and Chin, George. Temporal Graph Generation Based on a Distribution of Temporal Motifs. United States: N. p., 2018. Web.
Purohit, Sumit, Holder, Larry, & Chin, George. Temporal Graph Generation Based on a Distribution of Temporal Motifs. United States.
Purohit, Sumit, Holder, Larry, and Chin, George. Mon . "Temporal Graph Generation Based on a Distribution of Temporal Motifs". United States.
@article{osti_1507763,
title = {Temporal Graph Generation Based on a Distribution of Temporal Motifs},
author = {Purohit, Sumit and Holder, Larry and Chin, George},
abstractNote = {Generating a synthetic graph that is similar to a given real-world graph is a critical requirement for privacy preservation and benchmarking purposes. Various generative models attempt to generate static graphs similar to real-world graphs. However, generation of temporal graphs is still an open research area. We present a temporal-motif based approach to generate synthetic temporal graph datasets and results from three real-world use cases.},
doi = {},
journal = {},
number = ,
volume = ,
place = {United States},
year = {2018},
month = {8}
}

Conference:
Other availability
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