Svetlana Maltseva, Vasily Kornilov, Vladimir Barakhnin, and Alexander Gorbunov
Complexity Volume 2022 |Article ID 5714395
We can observe self-organization properties in various systems. However, modern networked dynamical sociotechnical systems have some features that allow for realizing the benefits of self-organization in a wide range of systems in economic and social areas. The review examines the general principles of self-organized systems, as well as the features of the implementation of self-organization in sociotechnical systems. We also delve into the production systems, in which the technical component is decisive, and social networks, in which the social component dominates; we analyze models used for modeling self-organizing networked dynamical systems. It is shown that discrete models prevail at the micro level. Furthermore, the review deals with the features of using continuous models for modeling at the macro level.
We’re delighted to crosspost this piece from the director of the Open Systems Lab, Alastair Parvin, an associate of a long-standing contributor here, Indy Johar. We’re particularly interested in the openness to new systems answering our crises coming from experiments below and outside even the
The growth of science and technology is a recombinative process, wherein new discoveries and inventions are built from prior knowledge. Yet relatively little is known about the manner in which scientific and technological knowledge develop and coalesce into larger structures that enable or constrain future breakthroughs. Network science has recently emerged as a framework for measuring the structure and dynamics of knowledge. While helpful, existing approaches struggle to capture the global properties of the underlying networks, leading to conflicting observations about the nature of scientific and technological progress. We bridge this methodological gap using tools from algebraic topology to characterize the higher-order structure of knowledge networks in science and technology across scale. We observe rapid growth in the higher-order structure of knowledge in many scientific and technological fields. This growth is not observable using traditional network measures. We further demonstrate that the emergence of higher-order structure coincides with decline in lower-order structure, and has historically far outpaced the corresponding emergence of higher-order structure in scientific and technological collaboration networks. Up to a point, increases in higher-order structure are associated with better outcomes, as measured by the novelty and impact of papers and patents. However, the nature of science and technology produced under higher-order regimes also appears to be qualitatively different from that produced under lower-order ones, with the former exhibiting greater linguistic abstractness and greater tendencies for building upon prior streams of knowledge.
Aming Li, Lei Zhou, Qi Su, Sean P. Cornelius, Yang-Yu Liu, Long Wang & Simon A. Levin Nature Communications volume 11, Article number: 2259 (2020)
Population structure is a key determinant in fostering cooperation among naturally self-interested individuals in microbial populations, social insect groups, and human societies. Traditional research has focused on static structures, and yet most real interactions are finite in duration and changing in time, forming a temporal network. This raises the question of whether cooperation can emerge and persist despite an intrinsically fragmented population structure. Here we develop a framework to study the evolution of cooperation on temporal networks. Surprisingly, we find that network temporality actually enhances the evolution of cooperation relative to comparable static networks, despite the fact that bursty interaction patterns generally impede cooperation. We resolve this tension by proposing a measure to quantify the amount of temporality in a network, revealing an intermediate level that maximally boosts cooperation. Our results open a new avenue for investigating the evolution of cooperation and other emergent behaviours in more realistic structured populations.
Ruochen Yang & Paul Bogdan Scientific Reports volume 10, Article number: 5541 (2020)
Mathematical modelling of real complex networks aims to characterize their architecture and decipher their underlying principles. Self-repeating patterns and multifractality exist in many real-world complex systems such as brain, genetic, geoscience, and social networks. To better comprehend the multifractal behavior in the real networks, we propose the weighted multifractal graph model to characterize the spatiotemporal complexity and heterogeneity encoded in the interaction weights. We provide analytical tools to verify the multifractal properties of the proposed model. By varying the parameters in the initial unit square, the model can reproduce a diverse range of multifractal spectrums with different degrees of symmetry, locations, support and shapes. We estimate and investigate the weighted multifractal graph model corresponding to two real-world complex systems, namely (i) the chromosome interactions of yeast cells in quiescence and in exponential growth, and (ii) the brain networks of cognitively healthy people and patients exhibiting late mild cognitive impairment leading to Alzheimer disease. The analysis of recovered models show that the proposed random graph model provides a novel way to understand the self-similar structure of complex networks and to discriminate different network structures. Additionally, by mapping real complex networks onto multifractal generating measures, it allows us to develop new network design and control strategies, such as the minimal control of multifractal measures of real systems under different functioning conditions or states.
A newly discovered connection between control theory and network dynamical systems could help estimate the size of a network even when a small portion is accessible.
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 macronodes, 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.
Human communication is invariably executed in the form of a narrative, an account of connected events comprising characters, actions, and settings. A coherent and well-structured narrative is therefore essential for effective communication, confusion caused by a haphazard attempt at storytelling being a common experience. This also suggests that a scientific understanding of how a narrative is formed and delivered is key to understanding human communication and dialog. Here we show that the definition of a narrative lends itself naturally to network-based modeling and analysis, and they can be further enriched by incorporating various text analysis methods from computational linguistics. We model the temporally unfolding nature of narrative as a dynamical growing network of nodes and edges representing characters and interactions, which allows us to characterize the story progression using the network growth pattern. We also introduce the concept of an interaction map between characters based on associated sentiments and topics identified from the text that characterize their relationships explicitly. We demonstrate the methods via application to Victor Hugo’s Les Misérables. Going beyond simple, aggregate occurrence-based methods for narrative representation and analysis, our proposed methods show promise in uncovering its essential nature of a highly complex, dynamic system that reflects the rich structure of human interaction and communication.
If you are interested in transforming systems so that they are good for everyone, then you are probably helping people co-create a System Shifting Network.
Humans communicate using systems of interconnected stimuli or concepts -- from language and music to literature and science -- yet it remains unclear how, if at all, the structure of these networks supports the communication of information. Although information theory provides tools to quantify the information produced by a system, traditional metrics do not account for the inefficient and biased ways that humans process this information. Here we develop an analytical framework to study the information generated by a system as perceived by a human observer. We demonstrate experimentally that this perceived information depends critically on a system's network topology. Applying our framework to several real networks, we find that they communicate a large amount of information (having high entropy) and do so efficiently (maintaining low divergence from human expectations). Moreover, we show that such efficient communication arises in networks that are simultaneously heterogeneous, with high-degree hubs, and clustered, with tightly-connected modules -- the two defining features of hierarchical organization. Together, these results suggest that many real networks are constrained by the pressures of information transmission, and that these pressures select for specific structural features.
Human information processing in complex networks
Christopher W. Lynn, Lia Papadopoulos, Ari E. Kahn, Danielle S. Bassett
Complex networks have been successfully used to describe the spread of diseases in populations of interacting individuals. Conversely, pairwise interactions are often not enough to characterize social contagion processes such as opinion formation or the adoption of novelties, where complex mechanisms of influence and reinforcement are at work. Here we introduce a higher-order model of social contagion in which a social system is represented by a simplicial complex and contagion can occur through interactions in groups of different sizes. Numerical simulations of the model on both empirical and synthetic simplicial complexes highlight the emergence of novel phenomena such as a discontinuous transition induced by higher-order interactions. We show analytically that the transition is discontinuous and that a bistable region appears where healthy and endemic states co-exist. Our results help explain why critical masses are required to initiate social changes and contribute to the understanding of higher-order interactions in complex systems.
Simplicial models of social contagion Iacopo Iacopini, Giovanni Petri, Alain Barrat & Vito Latora Nature Communications 10, Article number: 2485 (2019)
In studying successful nonprofit networks, I have found that there are four key operating principles that are critical to collaboration success. These soft skills are the ‘secret sauce’ that differentiates mediocre collaborations from those that achieve transformational change. Surprisingly, the operating principles of funders who have successfully supported networks differ dramatically from common practice in the philanthropic sector.
We have been taught to live in a competitive, survival-of-the-fittest world. But in reality, the natural world is mostly about cooperation. What can we learn from nature's mutualistic networks to help us make our human networks more resilient?
Instead of focusing on a few individuals, maybe we focus on the patterns of relationships, flows, and interactions in our complex human systems? What does emergence tell us? It is not the centrality of a few that matters, it is the weaving of many that is key to a successful community or organization!
Humans receive information from the world around them in
sequences of discrete items—from words in language or notes
in music to abstract concepts in books and websites on the
Internet. To model their environment, from a young age people
are tasked with learning the network structures formed by
these items (nodes) and the connections between them (edges).
But how do humans uncover the large-scale structures of networks when they experience only sequences of individual items?
Moreover, what do people’s internal maps and models of these
networks look like? Here, we introduce graph learning, a growing
and interdisciplinary field studying how humans learn and represent networks in the world around them. Specifically, we review progress toward understanding how people uncover the complex webs of relationships underlying sequences of items.
I am struck by how the network building and weaving field has really mushroomed over the past several years, and with it, so much learning around approaches, structures, roles, strategy, etc. I regularly hear myself say that there is no one right way to go about “net work” for change (which is why I regularly... Read More
A newly discovered connection between control theory and network dynamical systems could help estimate the size of a network even when a small portion is accessible.
Networks are not inherently more equitable or democratic. They operate within the same dominant white supremacist culture that undermines equity in our organizations.
If you are interested in transforming systems so that they are good for everyone, then you are probably helping people co-create a System Shifting Network.
On the eve of 20th century, three papers launched the modern Network Science by bringing it to the attention of a wider community of physicists, computer scientists and applied mathematicians. The papers - by Watts and Strogatz [1], Barabasi and Albert [2], and Google founders Brin and Page [3] - introduced “small world networks”, “preferential attachment,” and “PageRank” into the vernacular of network scientists. They showed that simple models could reproduce much of the complexity observed in network structure and that the structure of networks was linked to their function. As we mark the 20th anniversary of the publication of these seminal works, it is time to reflect on the state of Network Science and where the field is headed. What have we learned about networks over the past two decades? How does network structure affect its function? How do we represent networks, predict and control their behavior? How do networks grow and change? What are the limits of our understanding, and finally, what are the important open problems in network science?
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