Efficient Detection of Network Motifs

By Sebastian Wernicke. 2006

409 citations recorded by the source corpus.

Subfields: Molecular Biology, Statistical and Nonlinear Physics

How researchers see this paper

The researchers whose trusted networks rank it highest, strongest advocate first. Every researcher gets a different order.

In Molecular Biology

1. Nikolaus Schultz
2. Christopher Sander
3. Michael M. Bronstein
4. Stefanos P. Zafeiriou
5. Giovanni Ciriello
6. Ethan G. Cerami
7. Fabrizio Frasca
8. Giorgos Bouritsas

In Statistical and Nonlinear Physics

1. Fabrizio Frasca
2. Christopher Sander
3. Nikolaus Schultz
4. Michael M. Bronstein
5. Stefanos P. Zafeiriou
6. Giovanni Ciriello
7. Ethan G. Cerami
8. Giorgos Bouritsas

Citations in the corpus

• Improving Graph Neural Network Expressivity via Subgraph Isomorphism Counting (2020, methodology): “As a side note, approximate counting algorithms are also widely used, especially for counting frequent network motifs [58], [59], [60], [61], and can provide a considerable”
• Mutual exclusivity analysis identifies oncogenic network modules. (2012, methodology): “Specifically, we adopted the heuristic originally proposed by (Wernicke 2006) to extract all subgraphs of a given size from a network, and applied it to a method we developed to extract all maximal cliques independently of a size threshold.”