Reinforcement-Driven Spread of Innovations and Fads

We propose kinetic models for the spread of permanent innovations and transient fads by the mechanism of social reinforcement. Each individual can be in one of M+1 states of awareness 0,1,2,...,M, with state M corresponding to adopting an innovation. An individual with awareness k<M increases to k+1 by interacting with an adopter. Starting with a single adopter, the time for an initially unaware population of size N to adopt a permanent innovation grows as ln(N) for M=1, and as N^{1-1/M} for M>1. The fraction of the population that remains clueless about a transient fad after it has come and gone changes discontinuously as a function of the fad abandonment rate lambda for M>1. The fad dies out completely in a time that varies non-monotonically with lambda.Comment: 4 pages, 2 columns, 5 figures, revtex 4-1 format; revised version has been expanded and put into iop format, with one figure adde

Influence Diffusion in Social Networks under Time Window Constraints

We study a combinatorial model of the spread of influence in networks that generalizes existing schemata recently proposed in the literature. In our model, agents change behaviors/opinions on the basis of information collected from their neighbors in a time interval of bounded size whereas agents are assumed to have unbounded memory in previously studied scenarios. In our mathematical framework, one is given a network $G=(V,E)$, an integer value $t(v)$ for each node $v\in V$, and a time window size $\lambda$. The goal is to determine a small set of nodes (target set) that influences the whole graph. The spread of influence proceeds in rounds as follows: initially all nodes in the target set are influenced; subsequently, in each round, any uninfluenced node $v$ becomes influenced if the number of its neighbors that have been influenced in the previous $\lambda$ rounds is greater than or equal to $t(v)$. We prove that the problem of finding a minimum cardinality target set that influences the whole network $G$ is hard to approximate within a polylogarithmic factor. On the positive side, we design exact polynomial time algorithms for paths, rings, trees, and complete graphs.Comment: An extended abstract of a preliminary version of this paper appeared in: Proceedings of 20th International Colloquium on Structural Information and Communication Complexity (Sirocco 2013), Lectures Notes in Computer Science vol. 8179, T. Moscibroda and A.A. Rescigno (Eds.), pp. 141-152, 201

Layered social influence promotes multiculturality in the Axelrod model

9 pages, 4 figures, was "Robust multiculturality emerges from layered social influence". In press in Scientific Report

Timing interactions in social simulations: The voter model

The recent availability of huge high resolution datasets on human activities has revealed the heavy-tailed nature of the interevent time distributions. In social simulations of interacting agents the standard approach has been to use Poisson processes to update the state of the agents, which gives rise to very homogeneous activity patterns with a well defined characteristic interevent time. As a paradigmatic opinion model we investigate the voter model and review the standard update rules and propose two new update rules which are able to account for heterogeneous activity patterns. For the new update rules each node gets updated with a probability that depends on the time since the last event of the node, where an event can be an update attempt (exogenous update) or a change of state (endogenous update). We find that both update rules can give rise to power law interevent time distributions, although the endogenous one more robustly. Apart from that for the exogenous update rule and the standard update rules the voter model does not reach consensus in the infinite size limit, while for the endogenous update there exist a coarsening process that drives the system toward consensus configurations.Comment: Book Chapter, 23 pages, 9 figures, 5 table

An in silico structural approach to critical quality attributes assessment of biopharmaceutical products

\u201cQuality by design\u201d (QbD) is a key approach in modern pharmaceutical development, applied during the development, the manufacturing and the whole life cycle of the product, included the post approval phase, for assuring the quality in terms of efficacy and safety. In detail, QbD process includes the critical quality attributes (CQAs) assessment, providing a comprehensive understanding of the product itself and the manufacturing process. CQAs are defined as \u201call the physical, chemical, biological, or microbiological properties or characteristics that should be within an appropriate limit, range, or distribution to ensure the desired product quality\u201d (ICH Q8). They have a potential impact on bioactivity, PK, immunogenicity and safety and are associated with the drug substance and drug product. In the context of biotechnological products, the introduction of a structural investigation in an early identification of potential CQAs (pCQAs) can be very useful to QbD approach. Identification of pCQAs of biomolecules can lead the characterization process during the development phase in order to ensure the desired drug quality profile. Monoclonal antibodies (mAbs), fusion proteins and antibody-drug conjugates (ADC) represent one of the most innovative class of biopharmaceuticals, due to their ability to specifically recognize unique epitopes inducing specific therapeutic responses. CQAs assessment for these biopharmaceuticals is a complex analysis due to the lack of structural information. Actually, there is only one fully-crystallized human IgG1 (PDB entry: 1HZH) and, in absence of whole structures, it is challenging to understand the impact of structural insights on the therapeutic response. On these basis, the purpose of this study was to develop an in silico strategy to build the atomistic model of the whole structure of an IgG1, focusing on lambda and kappa light chains. To reach this goal, we used a structural chimeric approach that, using the Homology Modeling (HM) tool by MOE software, allowed us to build the full atomistic model of two therapeutic and commercially available IgG1: adalimumab (kappa chain) and avelumab (lambda chain). This allowed us to investigate structural differences between two isotypes, kappa and lambda, and understand the impact of these different characteristics on the antibody structure and function. Our results try to fill the gap between biological and structural properties on biotechnological products, created by lack of full immunoglobulin crystal structures. Moreover, this innovative structural approach can be used in CQAs assessment during the pharmaceutical development and production phases, giving an important resource to pharmaceutical companies. DISCLOSURES Merck Serono, Guidonia Montecelio-Rome, Italy is an affiliate of Merck KGaA, Darmstadt, Germany. Please note that avelumab has been approved in various countries for the treatment of metastatic Merkel cell carcinoma and in the US for treatment of advanced urothelial carcinoma progressed after platinum-containing treatment

Coevolution of Glauber-like Ising dynamics on typical networks

We consider coevolution of site status and link structures from two different initial networks: a one dimensional Ising chain and a scale free network. The dynamics is governed by a preassigned stability parameter $S$, and a rewiring factor $\phi$, that determines whether the Ising spin at the chosen site flips or whether the node gets rewired to another node in the system. This dynamics has also been studied with Ising spins distributed randomly among nodes which lie on a network with preferential attachment. We have observed the steady state average stability and magnetisation for both kinds of systems to have an idea about the effect of initial network topology. Although the average stability shows almost similar behaviour, the magnetisation depends on the initial condition we start from. Apart from the local dynamics, the global effect on the dynamics has also been studied. These parameters show interesting variations for different values of $S$ and $\phi$, which helps in determining the steady-state condition for a given substrate.Comment: 8 pages, 10 figure

From sparse to dense and from assortative to disassortative in online social networks

Inspired by the analysis of several empirical online social networks, we propose a simple reaction-diffusion-like coevolving model, in which individuals are activated to create links based on their states, influenced by local dynamics and their own intention. It is shown that the model can reproduce the remarkable properties observed in empirical online social networks; in particular, the assortative coefficients are neutral or negative, and the power law exponents are smaller than 2. Moreover, we demonstrate that, under appropriate conditions, the model network naturally makes transition(s) from assortative to disassortative, and from sparse to dense in their characteristics. The model is useful in understanding the formation and evolution of online social networks.Comment: 10 pages, 7 figures and 2 table

A Relational Event Approach to Modeling Behavioral Dynamics

This chapter provides an introduction to the analysis of relational event data (i.e., actions, interactions, or other events involving multiple actors that occur over time) within the R/statnet platform. We begin by reviewing the basics of relational event modeling, with an emphasis on models with piecewise constant hazards. We then discuss estimation for dyadic and more general relational event models using the relevent package, with an emphasis on hands-on applications of the methods and interpretation of results. Statnet is a collection of packages for the R statistical computing system that supports the representation, manipulation, visualization, modeling, simulation, and analysis of relational data. Statnet packages are contributed by a team of volunteer developers, and are made freely available under the GNU Public License. These packages are written for the R statistical computing environment, and can be used with any computing platform that supports R (including Windows, Linux, and Mac).