Inspired by empirical studies of networked systems such as the Internet, social networks, and biological networks, researchers have in recent years developed a variety of techniques and models to help us understand or predict the behavior of these systems. Here we review developments in this field, including such concepts as the small-world effect, degree distributions, clustering, network correlations, random graph models, models of network growth and preferential attachment, and dynamical processes taking place on networks.

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The Structure and Function of Complex Networks

We will review some of the major results in random graphs and some of the more challenging open problems. We will cover algorithmic and structural questions. We will touch on newer models, including those related to the WWW.

Random graphs

Evolutionary psychology is one of many biologically informed approaches to the study of human behavior. Along with cognitive psychologists, evolutionary psychologists propose that much, if not all, of our behavior can be explained by appeal to internal psychological mechanisms. What distinguishes evolutionary psychologists from many cognitive psychologists is the proposal that the relevant internal mechanisms are adaptations—products of natural selection—that helped our ancestors get around the world, survive and reproduce. To understand the central claims of evolutionary psychology we require an understanding of some key concepts in evolutionary biology, cognitive psychology, philosophy of science and philosophy of mind. Philosophers are interested in evolutionary psychology for a number of reasons. For philosophers of science —mostly philosophers of biology—evolutionary psychology provides a critical target. There is a broad consensus among philosophers of science that evolutionary psychology is a deeply flawed enterprise. For philosophers of mind and cognitive science evolutionary psychology has been a source of empirical hypotheses about cognitive architecture and specific components of that architecture. Philosophers of mind are also critical of evolutionary psychology but their criticisms are not as all-encompassing as those presented by philosophers of biology. Evolutionary psychology is also invoked by philosophers interested in moral psychology both as a source of empirical hypotheses and as a critical target.

/pdf/stanford-encyclopedia-of-philosophy-1wqoykisq0.pdf

Stanford Encyclopedia of Philosophy

Many ``real-world'' networks are clearly defined while most ``social'' networks are to some extent subjective. Indeed, the accuracy of empirically-determined social networks is a question of some concern because individuals may have distinct perceptions of what constitutes a social link. One unambiguous type of connection is sexual contact. Here we analyze data on the sexual behavior of a random sample of individuals, and find that the cumulative distributions of the number of sexual partners during the twelve months prior to the survey decays as a power law with similar exponents $\alpha \approx 2.4$ for females and males. The scale-free nature of the web of human sexual contacts suggests that strategic interventions aimed at preventing the spread of sexually-transmitted diseases may be the most efficient approach.

The Web of Human Sexual Contacts

How and why individuals adopt innovations has motivated a great deal of research. This article examines individuals’ computing adoption processes through the lenses of three adoption theories: Rogers’s innovation diffusion theory, the Concerns-Based Adoption Model, the Technology Acceptance Model, and the United Theory of Acceptance and Use of Technology. Incorporating all three models, this article suggests technology adoption is a complex, inherently social, developmental process; individuals construct unique yet malleable perceptions of technology that influence their adoption decisions. Thus, successfully facilitating technology adoption must address cognitive, emotional, and contextual concerns. This article also focuses specific attention on adoption theory outside of a formal organization and the implications of adoption theory on informal environments.

/pdf/understanding-technology-adoption-theory-and-future-2t4qiiqzzi.pdf

Understanding Technology Adoption: Theory and Future Directions for Informal Learning:

The authors propose an individual‐based model of innovation diffusion and explore its main dynamical properties. In the model, individuals assign an a priori social value to an innovation which evolves during their interactions with the “relative agreement” influence model. This model offers the possibility of including a minority of “extremists” with extreme and very definite opinions. Individuals who give a high social value to the innovation tend to look for information that allows them to evaluate more precisely the individual benefit of adoption. If the social value they assign is low, they neither consider the information nor transmit it. The main finding is that innovations with high social value and low individual benefit have a greater chance of succeeding than innovations with low social value and high individual benefit. Moreover, in some cases, a minority of extremists can have a very important impact on the propagation by polarizing the social value.

/pdf/an-individual-based-model-of-innovation-diffusion-mixing-2isyx398r6.pdf

An Individual‐Based Model of Innovation Diffusion Mixing Social Value and Individual Benefit1

Nexus: Small Worlds and the Groundbreaking Science of Networks by Mark Buchanan .

In this paper we approach the definition of new methodologies for the visualization and the exploration of social networks and their dynamics. We present a recently introduced formalism called TVG (for time-varying graphs), which was initially developed to model and analyze highly-dynamic and infrastructure-less communication networks, and TVG derived metrics. As an application context, we chose the case of scientific communities by analyzing a portion of the arXiv repository (ten years of publications in physics). We discuss the dataset by means of both static and temporal analysis of citations and co-authorships networks. Afterward, as we consider that scientific communities are at the same time communities of practice (through co-authorship) and that a citation represents a deliberative selection of a work among others, we introduce a new transformation to capture the co-existence of citations' effects and collaboration behaviors.

On the temporal analysis of scientific network evolution

Aiming at producing more realistic and informed agent-based simulations of social systems, one often need to build realistic synthetic populations. Apart of this synthetic population generation, the question of generating realistic social networks is an important phase. We examined the articles published in the Journal of Artificial Societies and Social Simulation (JASSS) in between 1998 and 2015 in order to identify the models of social networks that were actually used by the community. After presenting the main models (regular networks, random graphs, small-world networks, scale-free networks, spatial networks), we discuss the evolution of the use of each one of these models. We then present different existing alternatives to those kind of models and discuss the combined use of both simple and more elaborated or data-driven models to different aims along the process of developing agent-based social simulation with realistic synthetic populations.

/pdf/which-models-are-used-in-social-simulation-to-generate-3ghti88en4.pdf

Which models are used in social simulation to generate social networks?: a review of 17 years of publications in JASSS

One of the most interesting scientific challenges nowadays deals with the analysis and the understanding of complex networks' dynamics. A major issue is the definition of new frameworks for the exploration of the dynamics at play in real dynamic networks. Here, we focus on scientific communities by analyzing the "social part" of Science through a descriptive approach that aims at identifying the social determinants (e.g. goals and potential interactions among individuals) behind the emergence and the resilience of scientific communities. We consider that scientific communities are at the same time communities of practice (through co-authorship) and that they exist also as representations in the scientists' mind, since references to other scientists' works is not merely an objective link to a relevant work, but it reveals social objects that one manipulates and refers to. In this paper we identify the patterns about the evolution of a scientific field by analyzing a portion of the arXiv repository covering a period of 10 years of publications in physics. As a citation represents a deliberative selection related to the relevance of a work in its scientific domain, our analysis approaches the co-existence between co-authorship and citation behaviors in a community by focusing on the most proficient and cited authors interactions patterns. We focus in turn, on how these patterns are affected by the selection process of citations. Such a selection a) produces self-organization because it is played by a group of individuals which act, compete and collaborate in a common environment in order to advance Science and b) determines the success (emergence) of both topics and scientists working on them. The dataset is analyzed a) at a global level, e.g. the network evolution, b) at the meso-level, e.g. communities emergence, and c) at a micro-level, e.g. nodes' aggregation patterns.

pdf/selection-in-scientific-networks-3mlxm55c2l.pdf

Selection in Scientific Networks

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