Learning Change

Learning Change Project: 8 Blogs, +7400 Readings

Controllability of Complex Networks

The ultimate proof of our understanding of natural or technological systems is reflected in our ability to control them. Although control theory offers mathematical tools for steering engineered and natural systems towards a desired state, a framework to control complex self-organized systems is lacking. Here we develop analytical tools to study the controllability of an arbitrary complex directed network, identifying the set of driver nodes with time-dependent control that can guide the system’s entire dynamics. We apply these tools to several real networks, finding that the number of driver nodes is determined mainly by the network’s degree distribution. We show that sparse inhomogeneous networks, which emerge in many real complex systems, are the most difficult to control, but that dense and homogeneous networks can be controlled using a few driver nodes. Counterintuitively, we find that in both model and real systems the driver nodes tend to avoid the high-degree nodes.


Collective Response of Human Populations to Large- Scale Emergencies

Despite recent advances in uncovering the quantitative features of stationary human activity patterns, many applications, from pandemic prediction to emergency response, require an understanding of how these patterns change when the population encounters unfamiliar conditions. To explore societal response to external perturbations we identified real-time changes in communication and mobility patterns in the vicinity of eight emergencies, such as bomb attacks and earthquakes, comparing these with eight non-emergencies, like concerts and sporting events. We find that communication spikes accompanying emergencies are both spatially and temporally localized, but information about emergencies spreads globally, resulting in communication avalanches that engage in a significant manner the social network of eyewitnesses. These results offer a quantitative view of behavioral changes in human activity under extreme conditions, with potential long-term impact on emergency detection and response.


Uncovering the Role of Elementary Processes in Network Evolution

The growth and evolution of networks has elicited considerable interest from the scientific community and a number of mechanistic models have been proposed to explain their observed degree distributions. Various microscopic processes have been incorporated in these models, among them, node and edge addition, vertex fitness and the deletion of nodes and edges. The existing models, however, focus on specific combinations of these processes and parameterize them in a way that makes it difficult to elucidate the role of the individual elementary mechanisms. We therefore formulated and solved a model that incorporates the minimal processes governing network evolution. Some contribute to growth such as the formation of connections between existing pair of vertices, while others capture deletion; the removal of a node with its corresponding edges, or the removal of an edge between a pair of vertices. We distinguish between these elementary mechanisms, identifying their specific role on network evolution.


Written by Giorgio Bertini

November 24, 2015 at 5:00 pm

Community Structure of Complex Networks

Community structure is a salient structural characteristic of many real-world networks. Communities are generally hierarchical, overlapping, multi-scale and coexist with other types of structural regularities of networks. This poses major challenges for conventional methods of community detection. This book will comprehensively introduce the latest advances in community detection, especially the detection of overlapping and hierarchical community structures, the detection of multi-scale communities in heterogeneous networks, and the exploration of multiple types of structural regularities. These advances have been successfully applied to analyze large-scale online social networks, such as Facebook and Twitter. This book provides readers a convenient way to grasp the cutting edge of community detection in complex networks.

The thesis on which this book is based was honored with the “Top 100 Excellent Doctoral Dissertations Award” from the Chinese Academy of Sciences and was nominated as the “Outstanding Doctoral Dissertation” by the Chinese Computer Federation.


Human Mobility, Social Ties, and Link Prediction

Our understanding of how individual mobility patterns shape and impact the social network is limited, but is essential for a deeper understanding of network dynamics and evolution. This question is largely unexplored, partly due to the difficulty in obtaining large-scale society-wide data that simultaneously capture the dynamical information on individual movements and social interactions. Here we address this challenge for the first time by tracking the trajectories and communication records of 6 Million mobile phone users. We find that the similarity between two individuals’ movements strongly correlates with their proximity in the social network. We further investigate how the predictive power hidden in such correlations can be exploited to address a challenging problem: which new links will develop in a social network. We show that mobility measures alone yield surprising predictive power, comparable to traditional network-based measures. Furthermore, the prediction accuracy can be signicantly improved by learning a supervised classifier based on combined mobility and network measures. We believe our findings on the interplay of mobility patterns and social ties offer new perspectives on not only link prediction but also network dynamics.


Geographic Constraints on Social Network Groups

Social groups are fundamental building blocks of human societies. While our social interactions have always been constrained by geography, it has been impossible, due to practical difficulties, to evaluate the nature of this restriction on social group structure. We construct a social network of individuals whose most frequent geographical locations are also known. We also classify the individuals into groups according to a community detection algorithm. We study the variation of geographical span for social groups of varying sizes, and explore the relationship between topological positions and geographic positions of their members. We find that small social groups are geographically very tight, but become much more clumped when the group size exceeds about 30 members. Also, we find no correlation between the topological positions and geographic positions of individuals within network communities. These results suggest that spreading processes face distinct structural and spatial constraints.


Written by Giorgio Bertini

November 24, 2015 at 3:55 pm

Linked: The New Science of Networks

Linked: How Everything is Connected to Everything Else and What it Means for Business, Science, and Everyday Life.

Reductionism was the driving force behind much of the twentieth century’s scientific research. To comprehend nature, it tells us, we first must decipher its components. The assumption is that once we understand the parts, it will be easy to grasp the whole. Divide and conquer; the devil is in the details. Therefore, for decades we have been forced to see the world through its constituents. We have been trained to study atoms and superstrings to understand the universe; molecules to comprehend life; individual genes to understand complex human behavior; prophets to see the origins of fads and religions.

Now we are close to knowing just about everything there is to know about the pieces. But we are as far as we have ever been from understanding nature as a whole. Indeed, the reassembly turned out to be much harder than scientists anticipated. The reason is simple: Riding reductionism, we run into the hard wall of complexity. We have learned that nature is not a well-designed puzzle with only one way to put it back together. In complex systems the components can fit in so many different ways that it would take billions of years for us to try them all. Yet nature assembles the pieces with a grace and precision honed over millions of years. It does so by exploiting the allencompassing laws of self-organization, whose roots are still largely a mystery to us.


Written by Giorgio Bertini

November 24, 2015 at 3:27 pm

Network Science

Professor Barabási’s talk described how the tools of network science can help understand the Web’s structure, development and weaknesses. The Web is an information network, in which the nodes are documents (at the time of writing over one trillion of them), connected by links. Other well-known network structures include the Internet, a physical network where the nodes are routers and the links are physical connections, and organizations, where the nodes are people and the links represent communications.

As a result of studying these networks, Barabási argued that we have seen the emergence of network science, which overlaps with Web science. Network science is an attempt to understand networks emerging in nature, technology and society using a unified set of tools and principles. Despite apparent differences, many networks emerge and evolve, driven by a fundamental set of laws and mechanisms, and these are the province of network science.


Read also: Luck or reason

The network takeover

Written by Giorgio Bertini

November 24, 2015 at 2:58 pm

The Structure and Dynamics of Networks

From the Internet to networks of friendship, disease transmission, and even terrorism, the concept–and the reality–of networks has come to pervade modern society. But what exactly is a network? What different types of networks are there? Why are they interesting, and what can they tell us? In recent years, scientists from a range of fields–including mathematics, physics, computer science, sociology, and biology–have been pursuing these questions and building a new “science of networks.” This book brings together for the first time a set of seminal articles representing research from across these disciplines. It is an ideal sourcebook for the key research in this fast-growing field.

The book is organized into four sections, each preceded by an editors’ introduction summarizing its contents and general theme. The first section sets the stage by discussing some of the historical antecedents of contemporary research in the area. From there the book moves to the empirical side of the science of networks before turning to the foundational modeling ideas that have been the focus of much subsequent activity. The book closes by taking the reader to the cutting edge of network science–the relationship between network structure and system dynamics. From network robustness to the spread of disease, this section offers a potpourri of topics on this rapidly expanding frontier of the new science.


Written by Giorgio Bertini

November 24, 2015 at 10:19 am

Debates in the Digital Humanities

Welcome to the open-access edition of Debates in the Digital Humanities, which brings together leading figures in the field to explore its theories, methods, and practices and to clarify its multiple possibilities and tensions. Encompassing new technologies, research methods, and opportunities for collaborative scholarship and open-source peer review, as well as innovative ways of sharing knowledge and teaching, the digital humanities promises to transform the liberal arts—and perhaps the university itself. Indeed, at a time when many academic institutions are facing austerity budgets, digital humanities programs have been able to hire new faculty, establish new centers and initiatives, and attract multimillion-dollar grants. Clearly the digital humanities has reached a significant moment in its brief history. But what sort of moment is it? Debates in the Digital Humanities brings together leading figures in the field to explore its theories, methods, and practices and to clarify its multiple possibilities and tensions. From defining what a digital humanist is and determining whether the field has (or needs) theoretical grounding, to discussions of coding as scholarship and trends in data-driven research, this cutting-edge volume delineates the current state of the digital humanities and envisions potential futures and challenges. At the same time, several essays aim pointed critiques at the field for its lack of attention to race, gender, class, and sexuality; the inadequate level of diversity among its practitioners; its absence of political commitment; and its preference for research over teaching.


Written by Giorgio Bertini

November 23, 2015 at 4:39 pm


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