Real PPI networks used in our experiments.

<div><p>Testing or benchmarking network algorithms in bioinformatics requires a diverse set of networks with realistic properties. Real networks are often supplemented by randomly generated synthetic ones, but most graph generative models do not take into account the distribution of subg...

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Bibliographic Details
Main Author:	Bram Mornie (22127090) (author)
Other Authors:	Didier Colle (570226) (author), Pieter Audenaert (404604) (author), Mario Pickavet (404605) (author)
Published:	2025
Subjects:	Cancer Biological Sciences not elsewhere classified Mathematical Sciences not elsewhere classified Information Systems not elsewhere classified probabilistic target network probabilistic graph edges making small modifications instead derive bounds graph generative models benchmarking network algorithms 10 000 edges total node number update graphlet counts generating random graphs div >< p graphlet counts node graphlets uncertain events time independent subgraph patterns sparse graphs often supplemented often ignored motifs </ many cases incorrect conclusions graphlets taken graphlets </ experiments demonstrate every step diverse set different sizes different degrees controlled frequencies biological interactions bioinformatics requires
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Real PPI networks used in our experiments.

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