Welcome to the homepage of the Networks seminars, a weekly seminar series on networks, complex systems, and related topics held in the Mathematical Institute. In this year's series, we will alternate between regular talks and "fresh from the arXiv" talks (FFTA) in which we invite the author of a recently published (pre)print to discuss their work. Suggestions are always welcome!
The Networks seminar usually takes place on Tuesdays at 14:00-15:00 virtually on Zoom and a link to the event will be made available in the schedule of upcoming talks below (for logged-in users) and via the mailing list.
To sign up to our mailing list simply send an empty email to the following address:
If you would like to give a presentation at our seminar, please do not hesitate to contact the organisers Karel Devriendt or Rodrigo Leal Cervantes. The presentation can be either about your own work or on some (recent) interesting article on networks or on complex systems in general.
In case you missed any of the talks, we will also make recordings of the talks available on our youtube channel.
The complementarity and substitutability between products are essential concepts in retail and marketing. Qualitatively, two products are said to be substitutable if a customer can replace one product by the other, while they are complementary if they tend to be bought together. In this article, we take a network perspective to help automatically identify complements and substitutes from sales transaction data. Starting from a bipartite product-purchase network representation, with both transaction nodes and product nodes, we develop appropriate null models to infer significant relations, either complements or substitutes, between products, and design measures based on random walks to quantify their importance. The resulting unipartite networks between products are then analysed with community detection methods, in order to find groups of similar products for the different types of relationships. The results are validated by combining observations from a real-world basket dataset with the existing product hierarchy, as well as a large-scale flavour compound and recipe dataset.
arXiv link: https://arxiv.org/abs/2103.02042
- Networks Seminar
- Networks Seminar
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