Claudio Castellano

Statistical physics of social dynamics

Statistical physics has proven to be a fruitful framework to describe phenomena outside the realm of traditional physics. Recent years have witnessed an attempt by physicists to study collective phenomena emerging from the interactions of individuals as elementary units in social structures. A wide list of topics are reviewed ranging from opinion and cultural and language dynamics to crowd behavior, hierarchy formation, human dynamics, and social spreading. The connections between these problems and other, more traditional, topics of statistical physics are highlighted. Comparison of model results with empirical data from social systems are also emphasized.

Defining and identifying communities in networks

The investigation of community structures in networks is an important issue in many domains and disciplines. This problem is relevant for social tasks (objective analysis of relationships on the web), biological inquiries (functional studies in metabolic and protein networks), or technological problems (optimization of large infrastructures). Several types of algorithms exist for revealing the community structure in networks, but a general and quantitative definition of community is not implemented in the algorithms, leading to an intrinsic difficulty in the interpretation of the results without any additional nontopological information. In this article we deal with this problem by showing how quantitative definitions of community are implemented in practice in the existing algorithms. In this way the algorithms for the identification of the community structure become fully self-contained. Furthermore, we propose a local algorithm to detect communities which outperforms the existing algorithms with respect to computational cost, keeping the same level of reliability. The algorithm is tested on artificial and real-world graphs. In particular, we show how the algorithm applies to a network of scientific collaborations, which, for its size, cannot be attacked with the usual methods. This type of local algorithm could open the way to applications to large-scale technological and biological systems.

Epidemic processes in complex networks

In recent years the research community has accumulated overwhelming evidence for the emergence of complex and heterogeneous connectivity patterns in a wide range of biological and sociotechnical systems. The complex properties of real-world networks have a profound impact on the behavior of equilibrium and nonequilibrium phenomena occurring in various systems, and the study of epidemic spreading is central to our understanding of the unfolding of dynamical processes in complex networks. The theoretical analysis of epidemic spreading in heterogeneous networks requires the development of novel analytical frameworks, and it has produced results of conceptual and practical relevance. A coherent and comprehensive review of the vast research activity concerning epidemic processes is presented, detailing the successful theoretical approaches as well as making their limits and assumptions clear. Physicists, mathematicians, epidemiologists, computer, and social scientists share a common interest in studying epidemic spreading and rely on similar models for the description of the diffusion of pathogens, knowledge, and innovation. For this reason, while focusing on the main results and the paradigmatic models in infectious disease modeling, the major results concerning generalized social contagion processes are also presented. Finally, the research activity at the forefront in the study of epidemic spreading in coevolving, coupled, and time-varying networks is reported.

Universality of citation distributions: Toward an objective measure of scientific impact

We study the distributions of citations received by a single publication within several disciplines, spanning broad areas of science. We show that the probability that an article is cited c times has large variations between different disciplines, but all distributions are rescaled on a universal curve when the relative indicator cf = c/c0 is considered, where c0 is the average number of citations per article for the discipline. In addition we show that the same universal behavior occurs when citation distributions of articles published in the same field, but in different years, are compared. These findings provide a strong validation of cf as an unbiased indicator for citation performance across disciplines and years. Based on this indicator, we introduce a generalization of the h index suitable for comparing scientists working in different fields.

Thresholds for epidemic spreading in networks.

We study the threshold of epidemic models in quenched networks with degree distribution given by a power-law. For the susceptible-infected-susceptible model the activity threshold λ(c) vanishes in the large size limit on any network whose maximum degree k(max) diverges with the system size, at odds with heterogeneous mean-field (HMF) theory. The vanishing of the threshold has nothing to do with the scale-free nature of the network but stems instead from the largest hub in the system being active for any spreading rate λ>1/√k(max) and playing the role of a self-sustained source that spreads the infection to the rest of the system. The susceptible-infected-removed model displays instead agreement with HMF theory and a finite threshold for scale-rich networks. We conjecture that on quenched scale-rich networks the threshold of generic epidemic models is vanishing or finite depending on the presence or absence of a steady state.

Genotype-dependent sensitivity and tolerance to morphine and heroin. Dissociation between opiate-induced running and analgesia in the mouse.

Morphine and heroin-induced running activity (running fit), analgesia and tolerance were studied in the inbred strains of mice BALB/cJ, C57BL/6J and DBA/2J. Acute tolerance developed in all strains tested within 48 h. The effects of the opiates on the running fit and analgesia were strain-dependent and a negative strain correlation was evident between the two measures. The development and the recovery from acute tolerance were also genetically determined. It is concluded that the motor and analgesic effects of morphine in the mouse are two distinct phenomena and that the same neuronal and biochemical model cannot explain them. The results are discussed in relation to the brain regional and biochemical differences reported for these strains.

Nonequilibrium phase transition in a model for social influence.

We present extensive numerical simulations of the Axelrods model for social influence, aimed at understanding the formation of cultural domains. This is a nonequilibrium model with short range interactions and a remarkably rich dynamical behavior. We study the phase diagram of the model and uncover a nonequilibrium phase transition separating an ordered (culturally polarized) phase from a disordered (culturally fragmented) one. The nature of the phase transition can be continuous or discontinuous depending on the model parameters. At the transition, the size of cultural regions is power-law distributed.

Effects of morphine and heroin on discrimination learning and consolidation in mice

Morphine and heroin were administered to mice learning to swim toward a light source (l procedure) or toward the dark (d procedure), in a Y water maze, under pre- and post-trial drug treatment conditions.In the pre-trial experiments a clear disrupting effect on performance with the two procedures followed administration of both drugs, but for the l procedure, performance never fell below the 50% level of correct choices.Analysis of the performance within each session demonstrated a disruption in the long term memory consolidation mechanism. The administration of naloxone, or alternatively, discontinuation of the treatment, was followed by a gradual improvement, in performance by the treated animals.In both procedures, a performance disruption also followed the administration of the drugs immediately after each experimental session.

Post-training systemic and intra-amygdala administration of the GABA-B agonist baclofen impairs retention.

The effects of the GABA-B receptor agonist baclofen on memory storage were studied in two series of experiments. In the first series, CD-1 mice were trained in two aversively motivated tasks: a one-trial inhibitory avoidance task and a classical conditioning task (conditional emotional response). Immediate post-training ip administration of (+/-)baclofen (10 and 30 mg/kg) impaired retention of animals in both tasks. The effect was time-dependent: Retention was not affected by baclofen administered 120 min after training. In the second series of experiments, which used Sprague-Dawley rats, post-training intra-amygdala administration of baclofen impaired retention of an inhibitory avoidance response. These results support the view that the GABAergic system is involved in the modulation of memory storage and that the amygdaloid complex may be a critical site for effects of drugs affecting the GABAergic system.

D1 and D2 receptor antagonists differently affect cocaine-induced locomotor hyperactivity in the mouse

Pretreament with small, per se ineffective doses of the selective D1 antagonist SCH 23390 inhibited hyperactivity induced by cocaine. On the other hand, the classic neuroleptic haloperidol and the selective D2 antagonist metoclopramide prevented the stimulatory effects of cocaine on locomotion only at hypokinetic doses, while the atypical neuroleptic (−)-sulpiride, a selective D2 antagonist, did not produce significant effects when administered at the hypokinetic dose of 12 mg/kg. Finally, at low doses (−)-sulpiride dose-dependently potentiated the locomotor-stimulating effects of cocaine, an effect that is not shared either with haloperidol or with metoclopramide. These results are discussed in terms of different roles of DA receptor subtypes in the modulation of the stimulant effects of cocaine on locomotion.