Articles tagged with Network Dynamics
Researchers at FAU's CA-AI are developing machine learning technologies to enable autonomous systems to sense, learn, and act in coordination. The project aims to overcome the challenge of individual smart systems failing to collaborate effectively as part of a network.
A new theoretical framework shows that when interactions shift away from familiar contacts, activity can spread more widely. The study suggests that whether something spreads or stalls may hinge on a simple choice: revisit the same connections or explore new ones.
Researchers have shown that topology can guide multiple, information-carrying light signals through chip-based photonic communication systems, making them more powerful and reliable. This breakthrough could enable the creation of networks of chips that communicate using light while taking advantage of topology's robustness.
Researchers identified small clusters of interacting components that act as amplifiers, triggering outsized reactions in complex systems. These clusters can control how strongly a system reacts after a disruption, and their presence can determine the stability of entire systems.
Groups of tiny particles suspended in liquid oscillate together, keeping time as though they sense each other's motion. The surrounding fluid enables the particles to 'feel' one another at a distance, influencing their motions without direct contact.
Researchers develop Dual Perigee, a lightweight algorithm that streamlines network connections to enable secure and low-latency data sharing in IoT networks. The study reduces block-related delays by 48.54% compared to standard approaches.
A study on a 240-day closed experiment found that conflicts emerged among crew members, followed by stabilization through expert intervention. The blurring of boundaries between task-related and personal activities did not negatively impact team cohesion or performance levels.
A literature review reveals that our intuitions about success are often misleading, with failure being a predictor of future success and early successes reinforcing inequalities. The study also shows that persuading 'bridge' individuals in social networks is more effective at driving mass adoption than celebrity endorsements.
The study proposes a new framework for understanding complex higher-order networks, which could lead to breakthroughs in physics, neuroscience, computer science, and more. The framework integrates discrete topology and non-linear dynamics, offering insights into how topology shapes dynamics and evolves dynamically.
Researchers have found that higher-order interactions can lead to deeper basins of attraction, making solutions more stable, while also shrinking the basins themselves.
The Networks of Beliefs theory presents a comprehensive model of individual- and social-level belief dynamics, integrating personal, social, and external dissonances. By understanding these interplay dynamics, researchers can better grasp how beliefs change when we pay attention to different parts of our belief system.
A new study from North Carolina State University uncovers the crucial role of position in successful communication dynamics within a large organization. Researchers identified 'shortcuts' across the hierarchy that enable information to flow more quickly than traditional stepwise communication.
Researchers analyzed over 7,000 formal and informal bus routes across 36 cities and 22 countries. They found that informal transport systems self-organize to reach or exceed the efficiency level of centrally-planned services. The routes are often straight in central areas with fewer detours, making them efficient and profitable.
Researchers used supercomputers to study how fruit fly embryo cells develop into wings, offering a window into human development and possible treatments for birth defects. The team found that actomyosin drives much of the development process, particularly in the lower wing disc flattening.
New research has found that companies on the periphery of industry networks can successfully build connections and gain influence after disruptions. By examining airline industry networks, researchers discovered that peripheral airlines were more successful in building connections with central firms following major changes or shocks.
Politecnico researchers developed a new type of neural network called Latent Dynamics Network (LDNet) that can accurately predict the evolution of complex systems in low-dimensional spaces. This approach offers significant innovations over traditional methods, enabling up to 5 times more accurate results with a reduction of over 90% in...
A new analysis method can map individual company-level connections, revealing up to 13 billion supply connections worldwide. This data could reduce tax evasion (€130 billion) and improve climate and human rights compliance by creating a global supply network map.
Researchers at the University of Sydney and Max Planck Institute have developed new methods to describe synchronicity in complex systems. Their findings suggest that convergent walks on a network structure can lead to poor quality synchronisation.
The new edge sampling algorithm called E-MCBN makes sample results stable and balanced while preserving outliers. It adopts an edge-based and multi-class blue noise based sampling scheme to reduce visual clutter and improve the readability of MSV.
Bar-Ilan University researchers found that large and heterogeneous complex networks exhibit enhanced stability due to non-random patterns of interaction, contradicting Sir Robert May's original prediction. This discovery offers new guidelines for designing stable infrastructure networks and protecting fragile ecosystems.
Researchers discovered that even in the absence of predators, escape events occur frequently but rarely spread to many fish. The study suggests that this robustness to false alarms is due to dynamic adjustments in sensitivity to socially acquired information.
Researchers developed a model to infer opinions from social networks and measure left-right political division. The model shed new light on how political beliefs are shaped, providing insights for policymakers and real-world debates.
A research team used game theory to analyze cooperation in networks and found that networks with a high level of cooperation can emerge if individuals take a clear-cut position against free riders. The study also showed that if contributors leave an environment too quickly, it leads to a lower level of cooperation.
A research team from the Complexity Science Hub Vienna has developed a stress test to identify weaknesses and strengths in healthcare systems. They used data from Austria to show how many resident physicians can drop out before patients don't find a new doctor within reasonable distance, highlighting regional differences in resilience.
Researchers developed a novel computational method to control large complex networks using local information, addressing the challenge of controlling complex systems. The method considers computational time and information communication costs to produce optimal choices.
Bar-Ilan University researchers develop a two-step recovery process to revive failed networks by restructuring damaged links and reigniting the entire system through controlled node activation. This approach has shown promise in steering a failed microbiome back to functionality, particularly with probiotic interventions.
Researchers from the Complexity Science Hub Vienna propose that homophily, or interacting with like-minded individuals, automatically leads to social balance and stability. They demonstrate this using data from the Massive Multiplayer Online Game Pardus, where players tend to form friendships with those who share similar characteristics.
Researchers developed a novel scheme that reduces energy consumption while improving traffic prediction accuracy by up to 40% compared to benchmark schemes. The scheme uses software defined network and edge computing to control the operation of base stations, ensuring both sustainability and performance.
A transdisciplinary research team at Göttingen Campus has found a new perspective on the rhythmic processes in the brain. They discovered that adapting interneurons can switch between very slow rhythms and fast rhythms, challenging previous assumptions about their function.
Brain activity during wakefulness presents large integrated and dynamic network modules, which tend to vanish or fragment during sleep or under general anesthesia. The researchers built artificial time-evolving networks to test and benchmark algorithms for detecting these structures.
A study published in Nature Communications analyzed 360 million real-world ride requests from New York City and Chicago, revealing two adoption patterns that depend on demand. When demand is high, passengers are more likely to book a solo ride despite lower comfort levels.
Researchers have developed a superamphiphilic vitrimer epoxy resin membrane that can separate water and oil, making it an effective solution for cleaning up oil spills. The membrane is fully recyclable and can be depolymerized and reused after being clogged by pollutants.
A study by University of Pennsylvania researchers shows that people from independent cultures create similar category systems when working in large groups, but not in small groups. This challenges the idea that categories are innate and instead suggests that social network interactions shape our categorization methods.
Researchers created a musical ensemble of violinists to study human network dynamics. They found that humans can change their inner structure to reach better solutions, exhibiting unique characteristics like flexibility and filtering inputs.
A study by Heidelberg University and Max-Planck-Institute found that the distance to criticality can be adjusted in a brain-inspired chip, but only complex tasks benefit from it. Optimal network dynamics can be tuned using homeostatic plasticity by adapting mean input strength.
Researchers at the Complexity Science Hub Vienna developed a traffic light system to show global coronavirus infection trends. The tool helps users assess safe travel destinations by analyzing confirmed cases per 10,000 inhabitants, providing insights into the risks of traveling to countries with varying levels of infection.
Professor Maurizio Porfiri's research provides a model-free approach to determine the size of complex systems, even when only a small portion is accessible. For homogeneous networks, accessing just 10% of units can be sufficient to infer the entire network size.
Scientists at Tokyo Institute of Technology found that overly strong connections can invert the effect of connectivity on complex activity, leading to more regular patterns. This phenomenon is observed in various natural and engineered systems, including neurons, coupled oscillators, and wireless terminals.
Researchers developed a new method to identify significant ties in social networks, controlling for individual activity levels. This approach accurately mimics real-life school class clusters and has applications in various complex networks, including interbank markets and face-to-face interactions.
Researchers at CU Boulder discovered statistical rules governing fire ant colonies' flexibility, which may help craft smart polymers and swarming robots. The findings could also aid in understanding dynamic networks in nature, including human cells.
Researchers at EMBL have developed an interactive map of proteins involved in human cell division, allowing users to track protein dynamics and identify critical vulnerabilities. The tool has the potential to advance our understanding of cellular processes and disease mechanisms.
A team of scientists has developed a new analysis scheme to model dynamic cascading failures in power grids, taking into account the specific network dynamics between individual failures. This approach predicts potentially endangered lines and network components, enabling proactive risk assessments and system planning.
A new 'smart' algorithm is proposed to manage household energy usage, taking into account varying power sources like windmills and solar panels. The goal is to minimize costs while meeting energy demand, with the potential for future optimization through machine learning.
Researchers found that participants were more likely to collaborate when they could alter the structure of a network and had a pattern of relationships with multiple connections. This study aims to better explain the drivers of collaboration and has implications for various settings, including the workplace and the battlefield.
A study by University of Miami researchers found that individuals with specific patterns of salience network dynamics were better able to resist tempting distractors and perform tasks. The study, published in Social Cognitive and Affective Neuroscience, sheds new light on the neural mechanisms underlying self-control and temptation res...
Researchers are developing a networked airborne computing platform to facilitate the use of unmanned aerial systems (UAVs) for civilian applications. The platform will enable real-time data sharing and faster mission response times, increasing safety and efficiency.
Researchers at Stowers Institute for Medical Research used topological data analysis to identify new insights into the organization of complicated protein interaction networks. The study revealed topological network modules composed of proteins with shared properties, providing insight into biological functions and disease mechanisms.
Maurizio Porfiri, an associate professor at NYU-Poly, has been named the ASME Dynamic Systems and Controls Division Outstanding Young Investigator Award winner. His research on biomimetic robotic fish contributes to understanding animal collective behavior.
Researchers develop a new dynamic network management system that can redirect traffic-congesting connections in real-time, improving network efficiency. The system combines OpenFlow and PCE technology to provide efficient solution for operating transport networks.
Researchers found that parrots imitate unique calls to initiate conversations with specific individuals, responding more frequently and faster when mimicking an individual's call. In the wild, this ability helps orange-fronted conures communicate effectively in dynamic flocks.
Researchers from University of Warwick and Cambridge teamed up to apply fusion plasma expertise to study human brain networks. They developed methods to deal with data analysis problems similar to those faced by neuroscientists, making the first map of dynamically changing brain networks in response to surprise.
Rensselaer Polytechnic Institute assistant professor Xuegang (Jeff) Ban has received a prestigious NSF CAREER Award to study the potential of mobile devices in monitoring and optimizing traffic systems. His research aims to develop new methods for using cell phone and GPS data to improve traffic congestion and safety.
A mathematical analysis suggests that isolating terrorist network hubs is more effective than targeting the entire network. The study uses complex systems tools to analyze and model terrorist networks, finding that soft targets are not worth pursuing. Instead, focusing on the key hubs can lead to successful defeat.
Research reveals that dynamic semantic network of human language is small-world and scale-free but differs from syntactic network in hierarchical structure and node degree correlation. This discovery sheds light on the organization of human semantic knowledge and cognition.
Researchers at Virginia Tech will develop high-performance computer modeling tools to design efficient wireless spectrum markets. The goal is to create a robust, economically viable and efficient Dynamic Spectrum Access architecture for cellular networks.
Researchers developed an algorithm called XL that significantly reduces network routing updates, making it possible to support larger networks and improve efficiency for low-power devices with slow links. The algorithm uses three rules to determine which updates are important and can be suppressed.
Researchers investigated dynamic cortical networks in humans, confirming that familiar and unfamiliar objects activate distributed brain areas. They also measured directed information transfer between brain areas, revealing that most regions are involved in both receiving and sending signals during object recognition.
Murat Arcak, a researcher at Rensselaer Polytechnic Institute, received the 2007 SIAG/CST Prize for his work on passivity approach and robustness in largescale networks. His research has enhanced performance and robustness for wireless and biological networks.
Scientists at Max Planck Institute discovered that activity patterns on scale-free networks have unusual dynamic properties, robust against random perturbations but sensitive to selective ones. These networks can store and retrieve fixed patterns, making them suitable for associative memories and pattern recognition.
The study found functionally related genes were co-expressed across six distantly related organisms, including bacteria, yeast, and human. The researchers discovered conserved transcription modules that provide clues to the evolutionary building blocks generating diversity in cells.