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.

Social media treats all users the same: trusted friend or total stranger, with little or nothing in between. In reality, relationships fall everywhere along this spectrum, a topic social science has investigated for decades under the theme of tie strength. Our work bridges this gap between theory and practice. In this paper, we present a predictive model that maps social media data to tie strength. The model builds on a dataset of over 2,000 social media ties and performs quite well, distinguishing between strong and weak ties with over 85 % accuracy. We complement these quantitative findings with interviews that unpack the relationships we could not predict. The paper concludes by illustrating how modeling tie strength can improve social media design elements, including privacy controls, message routing, friend introductions and information prioritization. Author Keywords Social media, social networks, relationship modeling, ties,

The study of complex social systems has traditionally been an arduous process, involving extensive surveys, interviews, ethnographic studies, or analysis of online behavior. Today, however, it is possible to use the unprecedented amount of information generated by pervasive mobile phones to provide insights into the dynamics of both individual and group behavior. Information such as continuous proximity, location, communication and activity data, has been gathered from the phones of 100 human subjects at MIT. Systematic measurements from these 100 people over the course of eight months have generated one of the largest datasets of continuous human behavior ever collected, representing over 300,000 hours of daily activity. In this thesis we describe how this data can be used to uncover regular rules and structure in behavior of both individuals and organizations, infer relationships between subjects, verify selfreport

Research in organizational learning has demonstrated processes and occasionally performance implications of acquisition of declarative (know-what) and procedural (know-how) knowledge. However, considerably less attention has been paid to learned characteristics of relationships that affect the decision to seek information from other people. Based on a review of the social network, information processing, and organizational learning literatures, along with the results of a previous qualitative study, we propose a formal model of information seeking in which the probability of seeking information from another person is a function of (1) knowing what that person knows; (2) valuing what that person knows; (3) being able to gain timely access to that person’s thinking; and (4) perceiving that seeking information from that person would not be too costly. We also hypothesize that the knowing, access, and cost variables mediate the relationship between physical proximity and information seeking. The model is tested using two separate research sites to provide replication. The results indicate strong support for the model and the mediation hypothesis (with the exception of the cost variable). Implications are drawn for the study of both transactive memory and organizational learning, as well as for management practice.

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We perform sensitivity analyses to assess the impact of missing data on the structural properties of social networks. The social network is conceived of as being generated by a bipartite graph, in which actors are linked together via multiple interaction contexts or affiliations. We discuss three principal missing data mechanisms: network boundary specification (non-inclusion of actors or affiliations), survey non-response, and censoring by vertex degree (fixed choice design), examining their impact on the scientific collaboration network from the Los Alamos E-print Archive as well as random bipartite graphs. The simulation results show that network boundary specification and fixed choice designs can dramatically alter estimates of network-level statistics. The observed clustering and assortativity coefficients are overestimated via omission of affiliations or fixed choice thereof, and underestimated via actor non-response, which results in inflated measurement error. We also find that social networks with multiple interaction contexts may have certain interesting properties due to the presence of overlapping cliques. In particular, assortativity by degree does not necessarily improve network robustness to random omission of nodes as predicted by current theory.

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Reducing the physical distance among R&D engineers and between R&D and marketing is widely believed to result in more frequent communication, and hence higher product development performance. However, the empirical evidence for the effect of co-location on communication frequency is problematic for two reasons: (1) the evidence often features either little contextual realism or doubtful internal validity, and (2) the analysis does not deal with the statistical problems typical of network data. Our study avoids the first problem by using sequential network data collected from a quasi-experiment at an industrial company that regrouped its R&D teams into a new facility. We avoid the second problem by using Wasserman and Iacobucci's (1988) method for the statistical analysis of sequential network data. Our results show that communication among R&D teams was enhanced after co-locating these teams. Surprisingly, communication frequency between R&D and marketing was not affected by the increa...

This paper presents a data collection effort designed to incorporate the social dimension in social activity-travel behaviour, explicitly studying the link between individuals ’ social activities and their social networks. Using survey and interview instruments, the data collects the respondents ’ social networks using an egocentric approach, constituted by the interplay between their individual social structure and their social activity-behaviour. More explicitly, individuals ’ networks are studied in their relationship with social activity-travel generation, spatial distribution, and information communication technology use (ICT). The resultant data set links in novel ways aspects that have been rarely studied together, providing a sound base of theory and method to study and hopefully give new insights about social activity-travel behaviour. Carrasco, Hogan, Wellman, and Miller 3 1.