Traditional models assume a simple rule: people connect with others like them. But our research goes further. We’ve created a model that separates local homophily—strong bonds within close-knit groups—from global homophily, the weaker links across broader communities. This distinction helps explain complex social behaviors and how they impact network dynamics.
Using a maximum entropy approach, our model quantifies these layers of homophily and their influence on networks. One key finding is that different levels of homophily lead to unique percolation behaviors—shifts in how networks stay connected or fragment under certain conditions. We also discovered that these interactions affect critical thresholds for spreading phenomena, from viral outbreaks to information diffusion.
By applying our model to diverse real-world datasets, we demonstrated its ability to capture fine-grained patterns in networks. The insights go beyond theory—they have real implications for designing better public health interventions, optimizing information campaigns, and understanding the role of community structures in amplifying or limiting spread.
So, if you are looking for a network model that distinguishes between [local] homophily within small groups and [global] homophily across larger, more diverse communities, you shall not miss our new pre-print: https://arxiv.org/abs/2412.07901
Traditional models assume a simple rule: people connect with others like them. But our research goes further. We’ve created a model that separates local homophily—strong bonds within close-knit groups—from global homophily, the weaker links across broader communities. This distinction helps explain complex social behaviors and how they impact network dynamics.
Using a maximum entropy approach, our model quantifies these layers of homophily and their influence on networks. One key finding is that different levels of homophily lead to unique percolation behaviors—shifts in how networks stay connected or fragment under certain conditions. We also discovered that these interactions affect critical thresholds for spreading phenomena, from viral outbreaks to information diffusion.
By applying our model to diverse real-world datasets, we demonstrated its ability to capture fine-grained patterns in networks. The insights go beyond theory—they have real implications for designing better public health interventions, optimizing information campaigns, and understanding the role of community structures in amplifying or limiting spread.
So, if you are looking for a network model that distinguishes between [local] homophily within small groups and [global] homophily across larger, more diverse communities, you shall not miss our new pre-print: https://arxiv.org/abs/2412.07901
BY Complex Systems Studies
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The next bit isn’t clear, but Durov reportedly claimed that his resignation, dated March 21st, was an April Fools’ prank. TechCrunch implies that it was a matter of principle, but it’s hard to be clear on the wheres, whos and whys. Similarly, on April 17th, the Moscow Times quoted Durov as saying that he quit the company after being pressured to reveal account details about Ukrainians protesting the then-president Viktor Yanukovych. Artem Kliuchnikov and his family fled Ukraine just days before the Russian invasion. The channel appears to be part of the broader information war that has developed following Russia's invasion of Ukraine. The Kremlin has paid Russian TikTok influencers to push propaganda, according to a Vice News investigation, while ProPublica found that fake Russian fact check videos had been viewed over a million times on Telegram. On December 23rd, 2020, Pavel Durov posted to his channel that the company would need to start generating revenue. In early 2021, he added that any advertising on the platform would not use user data for targeting, and that it would be focused on “large one-to-many channels.” He pledged that ads would be “non-intrusive” and that most users would simply not notice any change. At this point, however, Durov had already been working on Telegram with his brother, and further planned a mobile-first social network with an explicit focus on anti-censorship. Later in April, he told TechCrunch that he had left Russia and had “no plans to go back,” saying that the nation was currently “incompatible with internet business at the moment.” He added later that he was looking for a country that matched his libertarian ideals to base his next startup.
from es
