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From social relationships to the main arteries of urban traffic, or from neuronal circuits to metabolic reactions in cells, the phenomena that influence our lives derive from complex interactions between multiple elements. These systems, defined as "complex", generate emergent phenomena that often cannot be understood by analyzing the activity of their components.
A typical example is “functional communities”, groups of highly correlated and interacting components with similar functions. These communities may include, for example, people with common interests, genes or proteins that collaborate for specific cellular functions, or niches of organisms that cooperate to maintain the ecological balance of their habitat.
In the study entitled “Unraveling the mesoscale organization induced by network-driven processes” published in PNAS, whose first author is Giacomo Barzon of the Department of Physics and Astronomy of the University of Padua and the Padua Neuroscience Center, to understand how the individual units of a system communicate with each other the authors investigated how small perturbations applied to each unit propagate over time within the complex networks of which they are part. This allowed us to reconstruct the "hidden geometry" of these phenomena, based on an effective distance measurement between the components. A short distance indicates effective communication between the components, while a large distance suggests they are probably functionally disconnected.
This framework has significant practical implications and offers a basis for future applications. For example, it can help understand how climate change could influence the dynamics of social or ecological communities, allowing its effects on the environment to be predicted and mitigated with targeted interventions. Or, again, in the context of social networks, it can provide insights into how increasingly polarized groups emerge and interact online, suggesting strategies to promote more constructive dialogue and mitigate social polarization.
“Thoroughly understanding the mechanisms and conditions that generate these phenomena is essential to understand the functioning of complex systems and develop targeted strategies to restore them when necessary – comments Giacomo Barzon, first author of the research -. Over the last thirty years, network science has focused on analyzing the structural connections between components, but this approach is not enough to explain the emergence of functional communities. It is crucial also to consider the specific physical phenomena that occur within networks and the environmental conditions that influence the speed and efficiency of these processes. In neurons, for example, temperature and chemical balance can modulate the speed of electrical signals, while in epidemics factors such as climate, population density, and social behavior can influence the spread of viruses. However, the specialized approach of different disciplines has limited the complete understanding of the emergence of these functional patterns.”
“Our methods are based on the statistical physics of complex networks, a fairly recent discipline but well represented by the research groups of our university, and in particular of the Department of Physics and Astronomy – continues Manlio De Domenico, professor of physics of complex networks of the University who led the international team –. Our study in the computational research laboratory, the CoMuNe Lab, in collaboration with the LIPh Lab and the University of Barcelona, has shown that many physical, chemical, biological, and social processes can be better understood in terms of functional modules that respond to the environment and are organized unexpectedly compared to what could be deduced from just knowing the microscopic scale of their components. The universality of these phenomena is one of the most studied, and even least understood, aspects of the physics of complex systems.”
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A typical example is “functional communities”, groups of highly correlated and interacting components with similar functions. These communities may include, for example, people with common interests, genes or proteins that collaborate for specific cellular functions, or niches of organisms that cooperate to maintain the ecological balance of their habitat.
In the study entitled “Unraveling the mesoscale organization induced by network-driven processes” published in PNAS, whose first author is Giacomo Barzon of the Department of Physics and Astronomy of the University of Padua and the Padua Neuroscience Center, to understand how the individual units of a system communicate with each other the authors investigated how small perturbations applied to each unit propagate over time within the complex networks of which they are part. This allowed us to reconstruct the "hidden geometry" of these phenomena, based on an effective distance measurement between the components. A short distance indicates effective communication between the components, while a large distance suggests they are probably functionally disconnected.
This framework has significant practical implications and offers a basis for future applications. For example, it can help understand how climate change could influence the dynamics of social or ecological communities, allowing its effects on the environment to be predicted and mitigated with targeted interventions. Or, again, in the context of social networks, it can provide insights into how increasingly polarized groups emerge and interact online, suggesting strategies to promote more constructive dialogue and mitigate social polarization.
“Thoroughly understanding the mechanisms and conditions that generate these phenomena is essential to understand the functioning of complex systems and develop targeted strategies to restore them when necessary – comments Giacomo Barzon, first author of the research -. Over the last thirty years, network science has focused on analyzing the structural connections between components, but this approach is not enough to explain the emergence of functional communities. It is crucial also to consider the specific physical phenomena that occur within networks and the environmental conditions that influence the speed and efficiency of these processes. In neurons, for example, temperature and chemical balance can modulate the speed of electrical signals, while in epidemics factors such as climate, population density, and social behavior can influence the spread of viruses. However, the specialized approach of different disciplines has limited the complete understanding of the emergence of these functional patterns.”
“Our methods are based on the statistical physics of complex networks, a fairly recent discipline but well represented by the research groups of our university, and in particular of the Department of Physics and Astronomy – continues Manlio De Domenico, professor of physics of complex networks of the University who led the international team –. Our study in the computational research laboratory, the CoMuNe Lab, in collaboration with the LIPh Lab and the University of Barcelona, has shown that many physical, chemical, biological, and social processes can be better understood in terms of functional modules that respond to the environment and are organized unexpectedly compared to what could be deduced from just knowing the microscopic scale of their components. The universality of these phenomena is one of the most studied, and even least understood, aspects of the physics of complex systems.”
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From social relationships to the main arteries of urban traffic, or from neuronal circuits to metabolic reactions in cells, the phenomena that influence our lives derive from complex interactions between multiple elements. These systems, defined as "complex", generate emergent phenomena that often cannot be understood by analyzing the activity of their components.
A typical example is “functional communities”, groups of highly correlated and interacting components with similar functions. These communities may include, for example, people with common interests, genes or proteins that collaborate for specific cellular functions, or niches of organisms that cooperate to maintain the ecological balance of their habitat.
In the study entitled “Unraveling the mesoscale organization induced by network-driven processes” published in PNAS, whose first author is Giacomo Barzon of the Department of Physics and Astronomy of the University of Padua and the Padua Neuroscience Center, to understand how the individual units of a system communicate with each other the authors investigated how small perturbations applied to each unit propagate over time within the complex networks of which they are part. This allowed us to reconstruct the "hidden geometry" of these phenomena, based on an effective distance measurement between the components. A short distance indicates effective communication between the components, while a large distance suggests they are probably functionally disconnected.
This framework has significant practical implications and offers a basis for future applications. For example, it can help understand how climate change could influence the dynamics of social or ecological communities, allowing its effects on the environment to be predicted and mitigated with targeted interventions. Or, again, in the context of social networks, it can provide insights into how increasingly polarized groups emerge and interact online, suggesting strategies to promote more constructive dialogue and mitigate social polarization.
“Thoroughly understanding the mechanisms and conditions that generate these phenomena is essential to understand the functioning of complex systems and develop targeted strategies to restore them when necessary – comments Giacomo Barzon, first author of the research -. Over the last thirty years, network science has focused on analyzing the structural connections between components, but this approach is not enough to explain the emergence of functional communities. It is crucial also to consider the specific physical phenomena that occur within networks and the environmental conditions that influence the speed and efficiency of these processes. In neurons, for example, temperature and chemical balance can modulate the speed of electrical signals, while in epidemics factors such as climate, population density, and social behavior can influence the spread of viruses. However, the specialized approach of different disciplines has limited the complete understanding of the emergence of these functional patterns.”
“Our methods are based on the statistical physics of complex networks, a fairly recent discipline but well represented by the research groups of our university, and in particular of the Department of Physics and Astronomy – continues Manlio De Domenico, professor of physics of complex networks of the University who led the international team –. Our study in the computational research laboratory, the CoMuNe Lab, in collaboration with the LIPh Lab and the University of Barcelona, has shown that many physical, chemical, biological, and social processes can be better understood in terms of functional modules that respond to the environment and are organized unexpectedly compared to what could be deduced from just knowing the microscopic scale of their components. The universality of these phenomena is one of the most studied, and even least understood, aspects of the physics of complex systems.”
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A typical example is “functional communities”, groups of highly correlated and interacting components with similar functions. These communities may include, for example, people with common interests, genes or proteins that collaborate for specific cellular functions, or niches of organisms that cooperate to maintain the ecological balance of their habitat.
In the study entitled “Unraveling the mesoscale organization induced by network-driven processes” published in PNAS, whose first author is Giacomo Barzon of the Department of Physics and Astronomy of the University of Padua and the Padua Neuroscience Center, to understand how the individual units of a system communicate with each other the authors investigated how small perturbations applied to each unit propagate over time within the complex networks of which they are part. This allowed us to reconstruct the "hidden geometry" of these phenomena, based on an effective distance measurement between the components. A short distance indicates effective communication between the components, while a large distance suggests they are probably functionally disconnected.
This framework has significant practical implications and offers a basis for future applications. For example, it can help understand how climate change could influence the dynamics of social or ecological communities, allowing its effects on the environment to be predicted and mitigated with targeted interventions. Or, again, in the context of social networks, it can provide insights into how increasingly polarized groups emerge and interact online, suggesting strategies to promote more constructive dialogue and mitigate social polarization.
“Thoroughly understanding the mechanisms and conditions that generate these phenomena is essential to understand the functioning of complex systems and develop targeted strategies to restore them when necessary – comments Giacomo Barzon, first author of the research -. Over the last thirty years, network science has focused on analyzing the structural connections between components, but this approach is not enough to explain the emergence of functional communities. It is crucial also to consider the specific physical phenomena that occur within networks and the environmental conditions that influence the speed and efficiency of these processes. In neurons, for example, temperature and chemical balance can modulate the speed of electrical signals, while in epidemics factors such as climate, population density, and social behavior can influence the spread of viruses. However, the specialized approach of different disciplines has limited the complete understanding of the emergence of these functional patterns.”
“Our methods are based on the statistical physics of complex networks, a fairly recent discipline but well represented by the research groups of our university, and in particular of the Department of Physics and Astronomy – continues Manlio De Domenico, professor of physics of complex networks of the University who led the international team –. Our study in the computational research laboratory, the CoMuNe Lab, in collaboration with the LIPh Lab and the University of Barcelona, has shown that many physical, chemical, biological, and social processes can be better understood in terms of functional modules that respond to the environment and are organized unexpectedly compared to what could be deduced from just knowing the microscopic scale of their components. The universality of these phenomena is one of the most studied, and even least understood, aspects of the physics of complex systems.”
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A typical example is “functional communities”, groups of highly correlated and interacting components with similar functions. These communities may include, for example, people with common interests, genes or proteins that collaborate for specific cellular functions, or niches of organisms that cooperate to maintain the ecological balance of their habitat.
In the study entitled “Unraveling the mesoscale organization induced by network-driven processes” published in PNAS, whose first author is Giacomo Barzon of the Department of Physics and Astronomy of the University of Padua and the Padua Neuroscience Center, to understand how the individual units of a system communicate with each other the authors investigated how small perturbations applied to each unit propagate over time within the complex networks of which they are part. This allowed us to reconstruct the "hidden geometry" of these phenomena, based on an effective distance measurement between the components. A short distance indicates effective communication between the components, while a large distance suggests they are probably functionally disconnected.
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“Thoroughly understanding the mechanisms and conditions that generate these phenomena is essential to understand the functioning of complex systems and develop targeted strategies to restore them when necessary – comments Giacomo Barzon, first author of the research -. Over the last thirty years, network science has focused on analyzing the structural connections between components, but this approach is not enough to explain the emergence of functional communities. It is crucial also to consider the specific physical phenomena that occur within networks and the environmental conditions that influence the speed and efficiency of these processes. In neurons, for example, temperature and chemical balance can modulate the speed of electrical signals, while in epidemics factors such as climate, population density, and social behavior can influence the spread of viruses. However, the specialized approach of different disciplines has limited the complete understanding of the emergence of these functional patterns.”
“Our methods are based on the statistical physics of complex networks, a fairly recent discipline but well represented by the research groups of our university, and in particular of the Department of Physics and Astronomy – continues Manlio De Domenico, professor of physics of complex networks of the University who led the international team –. Our study in the computational research laboratory, the CoMuNe Lab, in collaboration with the LIPh Lab and the University of Barcelona, has shown that many physical, chemical, biological, and social processes can be better understood in terms of functional modules that respond to the environment and are organized unexpectedly compared to what could be deduced from just knowing the microscopic scale of their components. The universality of these phenomena is one of the most studied, and even least understood, aspects of the physics of complex systems.”
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A typical example is “functional communities”, groups of highly correlated and interacting components with similar functions. These communities may include, for example, people with common interests, genes or proteins that collaborate for specific cellular functions, or niches of organisms that cooperate to maintain the ecological balance of their habitat.
In the study entitled “Unraveling the mesoscale organization induced by network-driven processes” published in PNAS, whose first author is Giacomo Barzon of the Department of Physics and Astronomy of the University of Padua and the Padua Neuroscience Center, to understand how the individual units of a system communicate with each other the authors investigated how small perturbations applied to each unit propagate over time within the complex networks of which they are part. This allowed us to reconstruct the "hidden geometry" of these phenomena, based on an effective distance measurement between the components. A short distance indicates effective communication between the components, while a large distance suggests they are probably functionally disconnected.
This framework has significant practical implications and offers a basis for future applications. For example, it can help understand how climate change could influence the dynamics of social or ecological communities, allowing its effects on the environment to be predicted and mitigated with targeted interventions. Or, again, in the context of social networks, it can provide insights into how increasingly polarized groups emerge and interact online, suggesting strategies to promote more constructive dialogue and mitigate social polarization.
“Thoroughly understanding the mechanisms and conditions that generate these phenomena is essential to understand the functioning of complex systems and develop targeted strategies to restore them when necessary – comments Giacomo Barzon, first author of the research -. Over the last thirty years, network science has focused on analyzing the structural connections between components, but this approach is not enough to explain the emergence of functional communities. It is crucial also to consider the specific physical phenomena that occur within networks and the environmental conditions that influence the speed and efficiency of these processes. In neurons, for example, temperature and chemical balance can modulate the speed of electrical signals, while in epidemics factors such as climate, population density, and social behavior can influence the spread of viruses. However, the specialized approach of different disciplines has limited the complete understanding of the emergence of these functional patterns.”
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A typical example is “functional communities”, groups of highly correlated and interacting components with similar functions. These communities may include, for example, people with common interests, genes or proteins that collaborate for specific cellular functions, or niches of organisms that cooperate to maintain the ecological balance of their habitat.
In the study entitled “Unraveling the mesoscale organization induced by network-driven processes” published in PNAS, whose first author is Giacomo Barzon of the Department of Physics and Astronomy of the University of Padua and the Padua Neuroscience Center, to understand how the individual units of a system communicate with each other the authors investigated how small perturbations applied to each unit propagate over time within the complex networks of which they are part. This allowed us to reconstruct the "hidden geometry" of these phenomena, based on an effective distance measurement between the components. A short distance indicates effective communication between the components, while a large distance suggests they are probably functionally disconnected.
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“Thoroughly understanding the mechanisms and conditions that generate these phenomena is essential to understand the functioning of complex systems and develop targeted strategies to restore them when necessary – comments Giacomo Barzon, first author of the research -. Over the last thirty years, network science has focused on analyzing the structural connections between components, but this approach is not enough to explain the emergence of functional communities. It is crucial also to consider the specific physical phenomena that occur within networks and the environmental conditions that influence the speed and efficiency of these processes. In neurons, for example, temperature and chemical balance can modulate the speed of electrical signals, while in epidemics factors such as climate, population density, and social behavior can influence the spread of viruses. However, the specialized approach of different disciplines has limited the complete understanding of the emergence of these functional patterns.”
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A typical example is “functional communities”, groups of highly correlated and interacting components with similar functions. These communities may include, for example, people with common interests, genes or proteins that collaborate for specific cellular functions, or niches of organisms that cooperate to maintain the ecological balance of their habitat.
In the study entitled “Unraveling the mesoscale organization induced by network-driven processes” published in PNAS, whose first author is Giacomo Barzon of the Department of Physics and Astronomy of the University of Padua and the Padua Neuroscience Center, to understand how the individual units of a system communicate with each other the authors investigated how small perturbations applied to each unit propagate over time within the complex networks of which they are part. This allowed us to reconstruct the "hidden geometry" of these phenomena, based on an effective distance measurement between the components. A short distance indicates effective communication between the components, while a large distance suggests they are probably functionally disconnected.
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“Thoroughly understanding the mechanisms and conditions that generate these phenomena is essential to understand the functioning of complex systems and develop targeted strategies to restore them when necessary – comments Giacomo Barzon, first author of the research -. Over the last thirty years, network science has focused on analyzing the structural connections between components, but this approach is not enough to explain the emergence of functional communities. It is crucial also to consider the specific physical phenomena that occur within networks and the environmental conditions that influence the speed and efficiency of these processes. In neurons, for example, temperature and chemical balance can modulate the speed of electrical signals, while in epidemics factors such as climate, population density, and social behavior can influence the spread of viruses. However, the specialized approach of different disciplines has limited the complete understanding of the emergence of these functional patterns.”
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A typical example is “functional communities”, groups of highly correlated and interacting components with similar functions. These communities may include, for example, people with common interests, genes or proteins that collaborate for specific cellular functions, or niches of organisms that cooperate to maintain the ecological balance of their habitat.
In the study entitled “Unraveling the mesoscale organization induced by network-driven processes” published in PNAS, whose first author is Giacomo Barzon of the Department of Physics and Astronomy of the University of Padua and the Padua Neuroscience Center, to understand how the individual units of a system communicate with each other the authors investigated how small perturbations applied to each unit propagate over time within the complex networks of which they are part. This allowed us to reconstruct the "hidden geometry" of these phenomena, based on an effective distance measurement between the components. A short distance indicates effective communication between the components, while a large distance suggests they are probably functionally disconnected.
This framework has significant practical implications and offers a basis for future applications. For example, it can help understand how climate change could influence the dynamics of social or ecological communities, allowing its effects on the environment to be predicted and mitigated with targeted interventions. Or, again, in the context of social networks, it can provide insights into how increasingly polarized groups emerge and interact online, suggesting strategies to promote more constructive dialogue and mitigate social polarization.
“Thoroughly understanding the mechanisms and conditions that generate these phenomena is essential to understand the functioning of complex systems and develop targeted strategies to restore them when necessary – comments Giacomo Barzon, first author of the research -. Over the last thirty years, network science has focused on analyzing the structural connections between components, but this approach is not enough to explain the emergence of functional communities. It is crucial also to consider the specific physical phenomena that occur within networks and the environmental conditions that influence the speed and efficiency of these processes. In neurons, for example, temperature and chemical balance can modulate the speed of electrical signals, while in epidemics factors such as climate, population density, and social behavior can influence the spread of viruses. However, the specialized approach of different disciplines has limited the complete understanding of the emergence of these functional patterns.”
“Our methods are based on the statistical physics of complex networks, a fairly recent discipline but well represented by the research groups of our university, and in particular of the Department of Physics and Astronomy – continues Manlio De Domenico, professor of physics of complex networks of the University who led the international team –. Our study in the computational research laboratory, the CoMuNe Lab, in collaboration with the LIPh Lab and the University of Barcelona, has shown that many physical, chemical, biological, and social processes can be better understood in terms of functional modules that respond to the environment and are organized unexpectedly compared to what could be deduced from just knowing the microscopic scale of their components. The universality of these phenomena is one of the most studied, and even least understood, aspects of the physics of complex systems.”
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A typical example is “functional communities”, groups of highly correlated and interacting components with similar functions. These communities may include, for example, people with common interests, genes or proteins that collaborate for specific cellular functions, or niches of organisms that cooperate to maintain the ecological balance of their habitat.
In the study entitled “Unraveling the mesoscale organization induced by network-driven processes” published in PNAS, whose first author is Giacomo Barzon of the Department of Physics and Astronomy of the University of Padua and the Padua Neuroscience Center, to understand how the individual units of a system communicate with each other the authors investigated how small perturbations applied to each unit propagate over time within the complex networks of which they are part. This allowed us to reconstruct the "hidden geometry" of these phenomena, based on an effective distance measurement between the components. A short distance indicates effective communication between the components, while a large distance suggests they are probably functionally disconnected.
This framework has significant practical implications and offers a basis for future applications. For example, it can help understand how climate change could influence the dynamics of social or ecological communities, allowing its effects on the environment to be predicted and mitigated with targeted interventions. Or, again, in the context of social networks, it can provide insights into how increasingly polarized groups emerge and interact online, suggesting strategies to promote more constructive dialogue and mitigate social polarization.
“Thoroughly understanding the mechanisms and conditions that generate these phenomena is essential to understand the functioning of complex systems and develop targeted strategies to restore them when necessary – comments Giacomo Barzon, first author of the research -. Over the last thirty years, network science has focused on analyzing the structural connections between components, but this approach is not enough to explain the emergence of functional communities. It is crucial also to consider the specific physical phenomena that occur within networks and the environmental conditions that influence the speed and efficiency of these processes. In neurons, for example, temperature and chemical balance can modulate the speed of electrical signals, while in epidemics factors such as climate, population density, and social behavior can influence the spread of viruses. However, the specialized approach of different disciplines has limited the complete understanding of the emergence of these functional patterns.”
“Our methods are based on the statistical physics of complex networks, a fairly recent discipline but well represented by the research groups of our university, and in particular of the Department of Physics and Astronomy – continues Manlio De Domenico, professor of physics of complex networks of the University who led the international team –. Our study in the computational research laboratory, the CoMuNe Lab, in collaboration with the LIPh Lab and the University of Barcelona, has shown that many physical, chemical, biological, and social processes can be better understood in terms of functional modules that respond to the environment and are organized unexpectedly compared to what could be deduced from just knowing the microscopic scale of their components. The universality of these phenomena is one of the most studied, and even least understood, aspects of the physics of complex systems.”
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A typical example is “functional communities”, groups of highly correlated and interacting components with similar functions. These communities may include, for example, people with common interests, genes or proteins that collaborate for specific cellular functions, or niches of organisms that cooperate to maintain the ecological balance of their habitat.
In the study entitled “Unraveling the mesoscale organization induced by network-driven processes” published in PNAS, whose first author is Giacomo Barzon of the Department of Physics and Astronomy of the University of Padua and the Padua Neuroscience Center, to understand how the individual units of a system communicate with each other the authors investigated how small perturbations applied to each unit propagate over time within the complex networks of which they are part. This allowed us to reconstruct the "hidden geometry" of these phenomena, based on an effective distance measurement between the components. A short distance indicates effective communication between the components, while a large distance suggests they are probably functionally disconnected.
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“Thoroughly understanding the mechanisms and conditions that generate these phenomena is essential to understand the functioning of complex systems and develop targeted strategies to restore them when necessary – comments Giacomo Barzon, first author of the research -. Over the last thirty years, network science has focused on analyzing the structural connections between components, but this approach is not enough to explain the emergence of functional communities. It is crucial also to consider the specific physical phenomena that occur within networks and the environmental conditions that influence the speed and efficiency of these processes. In neurons, for example, temperature and chemical balance can modulate the speed of electrical signals, while in epidemics factors such as climate, population density, and social behavior can influence the spread of viruses. However, the specialized approach of different disciplines has limited the complete understanding of the emergence of these functional patterns.”
“Our methods are based on the statistical physics of complex networks, a fairly recent discipline but well represented by the research groups of our university, and in particular of the Department of Physics and Astronomy – continues Manlio De Domenico, professor of physics of complex networks of the University who led the international team –. Our study in the computational research laboratory, the CoMuNe Lab, in collaboration with the LIPh Lab and the University of Barcelona, has shown that many physical, chemical, biological, and social processes can be better understood in terms of functional modules that respond to the environment and are organized unexpectedly compared to what could be deduced from just knowing the microscopic scale of their components. The universality of these phenomena is one of the most studied, and even least understood, aspects of the physics of complex systems.”
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A typical example is “functional communities”, groups of highly correlated and interacting components with similar functions. These communities may include, for example, people with common interests, genes or proteins that collaborate for specific cellular functions, or niches of organisms that cooperate to maintain the ecological balance of their habitat.
In the study entitled “Unraveling the mesoscale organization induced by network-driven processes” published in PNAS, whose first author is Giacomo Barzon of the Department of Physics and Astronomy of the University of Padua and the Padua Neuroscience Center, to understand how the individual units of a system communicate with each other the authors investigated how small perturbations applied to each unit propagate over time within the complex networks of which they are part. This allowed us to reconstruct the "hidden geometry" of these phenomena, based on an effective distance measurement between the components. A short distance indicates effective communication between the components, while a large distance suggests they are probably functionally disconnected.
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