Computational sociology

Computational sociology is a branch of sociology that uses computationally intensive methods to analyze and model social phenomena. Using computer simulations, artificial intelligence, complex statistical methods, and analytic approaches like social network analysis, computational sociology develops and tests theories of complex social processes through bottom-up modeling of social interactions. It involves the understanding of social agents, the interaction among these agents, and the effect of these interactions on the social aggregate. Although the subject matter and methodologies in social science differ from those in natural science or computer science, several of the approaches used in contemporary social simulation originated from fields such as physics and artificial intelligence. Some of the approaches that originated in this field have been imported into the natural sciences, such as measures of network centrality from the fields of social network analysis and network scien

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A short and versatile finite element multiscale code for homogenization problems

Assyr Abdulle, Achim Nonnenmacher

We describe a multiscale finite element (FE) solver for elliptic or parabolic problems with highly oscillating coefficients. Based on recent developments of the so-called heterogeneous multiscale method (HMM), the algorithm relies on coupled macro- and microsolvers. The framework of the HMM allows to design a code whose structure follows the classical finite elements implementation at the macro level. To account for the fine scales of the problem, elementwise numerical integration is replaced by micro FE methods on sampling domains. We discuss a short and flexible FE implementation of the multiscale algorithm, which can accommodate simplicial or quadrilateral FE and various coupling conditions for the constrained micro simulations. Extensive numerical examples including three dimensional and time dependent problems are presented illustrating the efficiency and the versatility of the computational strategy. (C) 2009 Elsevier B.V. All rights reserved.

2009

SPADnet: Embedded coincidence in a smart sensor network for PET applications

Claudio Bruschini, Edoardo Charbon, Gabor Nemeth

n this paper we illustrate the core technologies at the basis of the European SPADnet project (www.spadnet.eu), and present the corresponding first results. SPADnet is aimed at a new generation of MRI-compatible, scalable large area image sensors, based on CMOS technology, that are networked to perform gamma-ray detection and coincidence to be used primarily in (Time-of-Flight) Positron Emission Tomography (PET). The project innovates in several areas of PET systems, from optical coupling to single-photon sensor architectures, from intelligent ring networks to reconstruction algorithms. In addition, SPADnet introduced the first computational model enabling study of the full chain from gamma photons to network coincidence detection through scintillation events, optical coupling, etc.

Elsevier2014

Socio-Technical Network Analysis from Wearable Interactions

Rémi Emonet, Katayoun Farrahi

complexity and computational social sciences. This paper draws from explicit (phone calls, SMS messaging) and implicit (proximity sensing based on Bluetooth radio signals) interaction patterns collected via smartphones and reality mining techniques to explain the dynamics of personal interactions and relationships. We consider three real human to human interaction networks, namely physical proximity, phone communication and instant messaging. We analyze a real undergraduate community's social circles and consider various topologies, such as the interaction patterns of users with the entire community, and the interaction patterns of users within their own community. We fit distributions of various interactions, for example, showing that the distribution of users that have been in physical proximity but have never communicated by phone ts a gaussian. Finally, we consider the types of relationships for example friendships, to see whether significant differences exist in their interaction patterns. We find statistically significant differences in the physical proximity patterns of people who are mutual friends and people who are non-mutual (or asymmetric) friends, though this difference does not exist between mutual friends and never friends, nor does it exist in their phone communication patterns. Our findings impact a wide range of data-driven applications in socio-technical systems by providing an overview of community interaction patterns which can be used for applications such as epidemiology, or in understanding the diffusion of opinions and relationships.

2012

DH-500: Computational Social Media

The course integrates concepts from media studies, machine learning, multimedia and network science to characterize social practices and analyze content in sites like Facebook, Twitter and YouTube. Students will learn computational methods to infer individual and networked phenomena in social media.

AR-617: Data Think! (SwissUniversities)

Data Think! offers theoretical and practical introduction to data-centric research. Participants learn about collaborative research pipelines, data management plans, data ethics, data visualization and data valorisation strategies. We focus on the use of social, urban and environmental data.

AR-805: Data Think! (SwissUniversities)

Agent-based model

An agent-based model (ABM) is a computational model for simulating the actions and interactions of autonomous agents (both individual or collective entities such as organizations or groups) in order

Social simulation

Social simulation is a research field that applies computational methods to study issues in the social sciences. The issues explored include problems in computational law, psychology, organizational b

Sociology

Sociology is a social science that focuses on society, human social behavior, patterns of social relationships, social interaction, and aspects of culture associated with everyday life. In simple word