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Complex Systems Studies
Homophily Within and Across Groups 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://arxi…
How do similarities shape our connections—and what does that mean for spreading ideas, trends, or diseases?

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



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How do similarities shape our connections—and what does that mean for spreading ideas, trends, or diseases?

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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Recently, Durav wrote on his Telegram channel that users' right to privacy, in light of the war in Ukraine, is "sacred, now more than ever." The Security Service of Ukraine said in a tweet that it was able to effectively target Russian convoys near Kyiv because of messages sent to an official Telegram bot account called "STOP Russian War." "And that set off kind of a battle royale for control of the platform that Durov eventually lost," said Nathalie Maréchal of the Washington advocacy group Ranking Digital Rights. Again, in contrast to Facebook, Google and Twitter, Telegram's founder Pavel Durov runs his company in relative secrecy from Dubai. NEWS
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