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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Telegram was founded in 2013 by two Russian brothers, Nikolai and Pavel Durov. Following this, Sebi, in an order passed in January 2022, established that the administrators of a Telegram channel having a large subscriber base enticed the subscribers to act upon recommendations that were circulated by those administrators on the channel, leading to significant price and volume impact in various scrips. The company maintains that it cannot act against individual or group chats, which are “private amongst their participants,” but it will respond to requests in relation to sticker sets, channels and bots which are publicly available. During the invasion of Ukraine, Pavel Durov has wrestled with this issue a lot more prominently than he has before. Channels like Donbass Insider and Bellum Acta, as reported by Foreign Policy, started pumping out pro-Russian propaganda as the invasion began. So much so that the Ukrainian National Security and Defense Council issued a statement labeling which accounts are Russian-backed. Ukrainian officials, in potential violation of the Geneva Convention, have shared imagery of dead and captured Russian soldiers on the platform. But the Ukraine Crisis Media Center's Tsekhanovska points out that communications are often down in zones most affected by the war, making this sort of cross-referencing a luxury many cannot afford. These entities are reportedly operating nine Telegram channels with more than five million subscribers to whom they were making recommendations on selected listed scrips. Such recommendations induced the investors to deal in the said scrips, thereby creating artificial volume and price rise.
from br
