Summer reads 2: algorithms and real-world networks

For this second part we have collected our articles about algorithms and applications of the theory of networks.

Can algorithms learn?

How algorithms can learn from data is a very modern and hot topic. Want to read how this works? Then you can have a look at Bharti's article, where she describes how algorithms can classify images. But things can go wrong, sometimes in unexpected ways. Have a look at the article and read how mathematics can reveal weak spots of algorithms. In a second article, Wessel Blomerus makes a fictional character out of Reinforcement Learning who reflects upon some of his major achievements. To complete this topic, in his article Janusz Maylahn discusses if algorithms that are used to determine prices can learn to price collusively. In many cases, such algorithms are actually legal, even though they disfavour clients.

An image of Mr. Reinforcement Learning generated using DALL-E 2.

Some real world networks

Let us have a look at some articles about networks in applications. In his article, Mike van Santvoort, wrote about his research on communication networks between cells in our bodies. His goal is to understand how you can recostruct such a network when you have information from a biopsy, from say a tumor. Using analogies, amazing how vivid a story can become when you trust your creativity, Mike managed to write an article that is both educative but also fun to read!

To stay in biology, in his article, Martin Frohn wrote about phylogenetic networks and how they help understand mutations in the genomes of viruses.

If you prefer technological applications instead of biology, then you can have a look at the article of Andres Lopez Martinez, who wrote about distributed consensus. Do you want to know what money transactions and attacking armies have in common? Have a look at this article!

A concept closely related to consensus is dissemination of information, which is nowadays often determined by the algorithms used by social media platforms. For example, since 2016 the world has been witnessing the Rohingya genocide in Myanmar, in 2021 we all saw the Capitol riots on tv. These two are completely different geopolitical events, yet they have similarities. Namely, preceding and during both events, there was a storm of misinformation and propaganda spreading, both offline and online. In his article, Rounak Ray discussed how social networking sites played a role in this wave of misinformation!

Although Rounak's article showed how propaganda can spread through social network platforms, these same social networks can also help no-profit organizations reach out to a larger audience. In her article, Anna Priante wrote about her research on communication networks and how these can help fundraising in the no-profit sector!

From tweets to communication networks: The nodes in this network stand for people who have tweeted using #hashtags related to the Movember campaign. The edges are colored depending on the type of tweet: red for a simple mentioning, blue for retweets, green for answering, and orange for a common (unrelated) tweet.

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