text: 156,135,1149037159875170304,1046598080,Brennan Klein,"Almost 2 years ago to the day, @erikphoel and I met in NY to chat about his terrific paper on causal emergence <LINK> Since then, we’ve been building a formalism to study causal emergence in networks. Today we posted our first paper on it <LINK> <LINK> Networks are such powerful objects. They've changed how we study complex systems. But I’ve always been struck by how nontrivial the “what is a node?” question can be. We provide a framework for identifying the most informative *scale* to describe interdependencies in a system... <LINK> ...which is to say, we find that compressed or coarse-grained or macroscale descriptions of networks often have more *effective information* than the original microscale network (e.g. your raw network data). This noise-minimizing process is known as causal emergence. So what? <LINK> It's a question of zoom: what's the right scale to represent brain networks, given what we want from our model? What's the best scale to model economic systems? What counts as a ""node"" in a genome? They're rich, tough, fun problems. And there's a long way to go. <LINK> This research fascinates me, and there's a bunch of directions to go with it. Feel free to send feedback or questions, and stay tuned for the release of some tutorials / open python code. <LINK>",https://arxiv.org/abs/1907.03902,"The connectivity of a network contains information about the relationships between nodes, which can denote interactions, associations, or dependencies. We show that this information can be analyzed by measuring the uncertainty (and certainty) contained in paths along nodes and links in a network. Specifically, we derive from first principles a measure known as effective information and describe its behavior in common network models. Networks with higher effective information contain more information in the relationships between nodes. We show how subgraphs of nodes can be grouped into macro-nodes, reducing the size of a network while increasing its effective information (a phenomenon known as causal emergence). We find that informative higher scales are common in simulated and real networks across biological, social, informational, and technological domains. These results show that the emergence of higher scales in networks can be directly assessed and that these higher scales offer a way to create certainty out of uncertainty. ",The emergence of informative higher scales in complex networks,5,"['Almost 2 years ago to the day, @erikphoel and I met in NY to chat about his terrific paper on causal emergence <LINK>\n\nSince then, we’ve been building a formalism to study causal emergence in networks. Today we posted our first paper on it <LINK> <LINK>', ""Networks are such powerful objects. They've changed how we study complex systems. But I’ve always been struck by how nontrivial the “what is a node?” question can be.\n\nWe provide a framework for identifying the most informative *scale* to describe interdependencies in a system... https://t.co/FgxIUkQu36"", '...which is to say, we find that compressed or coarse-grained or macroscale descriptions of networks often have more *effective information* than the original microscale network (e.g. your raw network data).\n\nThis noise-minimizing process is known as causal emergence.\n\nSo what? https://t.co/WQ39KyHs5B', 'It\'s a question of zoom: what\'s the right scale to represent brain networks, given what we want from our model? What\'s the best scale to model economic systems? What counts as a ""node"" in a genome?\n\nThey\'re rich, tough, fun problems. And there\'s a long way to go. https://t.co/ZKnNUvmxF9', ""This research fascinates me, and there's a bunch of directions to go with it.\n\nFeel free to send feedback or questions, and stay tuned for the release of some tutorials / open python code.\n\nhttps://t.co/Til7g7LCEk""]",19,07,1288