Temporal Graph Generation Based on a Distribution of Temporal Motifs

Conference · Mon Aug 20 00:00:00 EDT 2018

OSTI ID:1507763

Purohit, Sumit  ^[1]; Holder, Larry ^[2]; Chin, George ^[1]

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1. BATTELLE (PACIFIC NW LAB)
2. Washington State University

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.

• Research Organization: Pacific Northwest National Lab. (PNNL), Richland, WA (United States)
• Sponsoring Organization: USDOE
• DOE Contract Number: AC05-76RL01830
• OSTI ID: 1507763
• Report Number(s): PNNL-SA-134797
• 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