![]() ![]() 239000008186 active pharmaceutical agent Substances 0.000 description 4.230000000875 corresponding Effects 0.000 claims abstract description 30.238000009826 distribution Methods 0.000 claims abstract description 108. ![]() Assignors: MICROSOFT CORPORATION Status Abandoned legal-status Critical Current Links Assignors: SHRIRAGHAV, KAUSHIK, ARASU, ARVIND, LI, JIAN Publication of US20120330880A1 publication Critical patent/US20120330880A1/en Assigned to MICROSOFT TECHNOLOGY LICENSING, LLC reassignment MICROSOFT TECHNOLOGY LICENSING, LLC ASSIGNMENT OF ASSIGNORS INTEREST (SEE DOCUMENT FOR DETAILS). Google has not performed a legal analysis and makes no representation as to the accuracy of the date listed.) Filing date Publication date Application filed by Microsoft Corp filed Critical Microsoft Corp Priority to US13/166,831 priority Critical patent/US20120330880A1/en Assigned to MICROSOFT CORPORATION reassignment MICROSOFT CORPORATION ASSIGNMENT OF ASSIGNORS INTEREST (SEE DOCUMENT FOR DETAILS). Original Assignee Microsoft Corp Priority date (The priority date is an assumption and is not a legal conclusion. ![]() Google has not performed a legal analysis and makes no representation or warranty as to the accuracy of the list.) Google has not performed a legal analysis and makes no representation as to the accuracy of the status listed.) Abandoned Application number US13/166,831 Inventor Arvind Arasu Kaushik Shriraghav Jian Li Current Assignee (The listed assignees may be inaccurate. Google Patents US20120330880A1 - Synthetic data generation Insights than overfitting on three citation datasets.US20120330880A1 - Synthetic data generation Generations allows for thorough investigation of algorithms and provides more We develop a fully-featured synthetic graph generator thatĪllows deep inspection of different models. Synthetic graphs, and study the behaviour of graph learning algorithms in aĬontrolled scenario. In this work, we aim to address this deficiency. Shockingly small sample size (~10) allows for only limited scientific insight Practice to benchmark the performance of graph learning algorithms. One reason is due to the very small number of datasets used in It has, however, become hard to track the field's burgeoning Graph analysis tasks such as node classification, link prediction, andĬlustering. Download a PDF of the paper titled Synthetic Graph Generation to Benchmark Graph Learning, by Anton Tsitsulin and 3 other authors Download PDF Abstract: Graph learning algorithms have attained state-of-the-art performance on many ![]()
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