Graph coarse-graining reveals differences in the module-level structure of functional brain networks.

Networks have become a standard tool for analyzing functional magnetic resonance imaging (fMRI) data. In this approach, brain areas and their functional connections are mapped to the nodes and links of a network. Even though this mapping reduces the complexity of the underlying data, it remains challenging to understand the structure of the resulting networks due to the large number of nodes and links.

Reorganization of functionally connected brain subnetworks in high-functioning autism.

Previous functional connectivity studies have found both hypo- and hyper-connectivity in brains of individuals having autism spectrum disorder (ASD). Here we studied abnormalities in functional brain subnetworks in high-functioning individuals with ASD during free viewing of a movie containing social cues and interactions. Twenty-six subjects (13 with ASD) watched a 68-min movie during functional magnetic resonance imaging.

Reputation and impact in academic careers.

Reputation is an important social construct in science, which enables informed quality assessments of both publications and careers of scientists in the absence of complete systemic information. However, the relation between reputation and career growth of an individual remains poorly understood, despite recent proliferation of quantitative research evaluation methods. Here, we develop an original framework for measuring how a publication's citation rate Δc depends on the reputation of its central author i, in addition to its net citation count c.

Inferring human mobility using communication patterns.

Understanding the patterns of mobility of individuals is crucial for a number of reasons, from city planning to disaster management. There are two common ways of quantifying the amount of travel between locations: by direct observations that often involve privacy issues, e.g.

Effects of temporal correlations on cascades: threshold models on temporal networks.

A person's decision to adopt an idea or product is often driven by the decisions of peers, mediated through a network of social ties. A common way of modeling adoption dynamics is to use threshold models, where a node may become an adopter given a high enough rate of contacts with adopted neighbors. We study the dynamics of threshold models that take both the network topology and the timings of contacts into account, using empirical contact sequences as substrates.

Author Impact Factor: tracking the dynamics of individual scientific impact.

The impact factor (IF) of scientific journals has acquired a major role in the evaluations of the output of scholars, departments and whole institutions. Typically papers appearing in journals with large values of the IF receive a high weight in such evaluations. However, at the end of the day one is interested in assessing the impact of individuals, rather than papers.

On the predictability of future impact in science.

Correctly assessing a scientist's past research impact and potential for future impact is key in recruitment decisions and other evaluation processes. While a candidate's future impact is the main concern for these decisions, most measures only quantify the impact of previous work. Recently, it has been argued that linear regression models are capable of predicting a scientist's future impact.

Contextual analysis framework for bursty dynamics.

To understand the origin of bursty dynamics in natural and social processes we provide a general analysis framework in which the temporal process is decomposed into subprocesses and then the bursts in subprocesses, called contextual bursts, are combined to collective bursts in the original process. For the combination of subprocesses, it is required to consider the distribution of different contexts over the original process. Based on minimal assumptions for interevent time statistics, we present a theoretical analysis for the relationship between contextual and collective interevent time distributions.

World citation and collaboration networks: uncovering the role of geography in science.

Modern information and communication technologies, especially the Internet, have diminished the role of spatial distances and territorial boundaries on the access and transmissibility of information. This has enabled scientists for closer collaboration and internationalization. Nevertheless, geography remains an important factor affecting the dynamics of science.

Time-varying priority queuing models for human dynamics.

Queuing models provide insight into the temporal inhomogeneity of human dynamics, characterized by the broad distribution of waiting times of individuals performing tasks. We theoretically study the queuing model of an agent trying to execute a task of interest, the priority of which may vary with time due to the agent's "state of mind." However, its execution is disrupted by other tasks of random priorities.

The evolution of interdisciplinarity in physics research.

Science, being a social enterprise, is subject to fragmentation into groups that focus on specialized areas or topics. Often new advances occur through cross-fertilization of ideas between sub-fields that otherwise have little overlap as they study dissimilar phenomena using different techniques. Thus to explore the nature and dynamics of scientific progress one needs to consider the organization and interactions between different subject areas.

Path lengths, correlations, and centrality in temporal networks.

In temporal networks, where nodes interact via sequences of temporary events, information or resources can only flow through paths that follow the time ordering of events. Such temporal paths play a crucial role in dynamic processes. However, since networks have so far been usually considered static or quasistatic, the properties of temporal paths are not yet well understood.

Emergence of bursts and communities in evolving weighted networks.

Understanding the patterns of human dynamics and social interaction and the way they lead to the formation of an organized and functional society are important issues especially for techno-social development. Addressing these issues of social networks has recently become possible through large scale data analysis of mobile phone call records, which has revealed the existence of modular or community structure with many links between nodes of the same community and relatively few links between nodes of different communities. The weights of links, e.

Using explosive percolation in analysis of real-world networks.

We apply a variant of the explosive percolation procedure to large real-world networks and show with finite-size scaling that the university class, ordinary or explosive, of the resulting percolation transition depends on the structural properties of the network, as well as the number of unoccupied links considered for comparison in our procedure. We observe that in our social networks, the percolation clusters close to the critical point are related to the community structure. This relationship is further highlighted by applying the procedure to model networks with predefined communities.

Mesoscopic organization reveals the constraints governing Caenorhabditis elegans nervous system.

One of the biggest challenges in biology is to understand how activity at the cellular level of neurons, as a result of their mutual interactions, leads to the observed behavior of an organism responding to a variety of environmental stimuli. Investigating the intermediate or mesoscopic level of organization in the nervous system is a vital step towards understanding how the integration of micro-level dynamics results in macro-level functioning. The coordination of many different co-occurring processes at this level underlies the command and control of overall network activity.

Phase of Ising spins on modular networks analogous to social polarization.

Coordination processes in complex systems can be related to the problem of collective ordering in networks, many of which have modular organization. Investigating the order-disorder transition for Ising spins on modular random networks, corresponding to consensus formation in society, we observe two distinct phases: (i) ordering within each module at a critical temperature followed by (ii) global ordering at a lower temperature. This indicates polarization of society into groups having contrary opinions can persist indefinitely even when mutual interactions between agents favor consensus.

Collective behavior of stock price movements in an emerging market.

To investigate the universality of the structure of interactions in different markets, we analyze the cross-correlation matrix C of stock price fluctuations in the National Stock Exchange (NSE) of India. We find that this emerging market exhibits strong correlations in the movement of stock prices compared to developed markets, such as the New York Stock Exchange (NYSE). This is shown to be due to the dominant influence of a common market mode on the stock prices.

Modular networks emerge from multiconstraint optimization.

Modular structure is ubiquitous among complex networks. We note that most such systems are subject to multiple structural and functional constraints, e.g.