Thank you for the support in getting Network Science up and running. We are deeply appreciative of the work our associate editors, authors, and reviewers have put into realizing this vision of an interdisciplinary journal for network science. And of course, the journal would not be possible without the hard work of the editors of Network Science, who act as action editors for the submitted articles.

We use data on a real, large-scale social network of 27 million individuals interacting daily, together with the day-by-day adoption of a new mobile service product, to inform, build, and analyze data-driven simulations of the effectiveness of seeding (network targeting) strategies under different social conditions. Three main results emerge from our simulations. First, failure to consider homophily creates significant overestimation of the effectiveness of seeding strategies, casting doubt on conclusions drawn by simulation studies that do not model homophily. Second, seeding is constrained by the small fraction of potential influencers that exist in the network. We find that seeding more than 0.2% of the population is wasteful because the gain from their adoption is lower than the gain from their natural adoption (without seeding). Third, seeding is more effective in the presence of greater social influence. Stronger peer influence creates a greater than additive effect when combined with seeding. Our findings call into question some conventional wisdom about these strategies and suggest that their overall effectiveness may be overestimated.

Self-identification with peer crowds (jocks, popular kids, druggies, etc.) has an important influence on adolescent substance use behavior. However, little is known about the impact of the shared nature of crowd identification on different stages of adolescent drinking behavior, or the way crowd identification interacts with participation in school-sponsored sports activities. This study examines drinking influences from (1) peers with shared crowd identities, and (2) peers who jointly participate in organized sports at their school (activity members). This study introduces a new network analytic approach that can disentangle the effects of crowd identification and sports participation on individual behavior. Using survey data from adolescents in five high schools in a predominantly Hispanic/Latino district (N = 1,707), this paper examines the association between social influences and each stage of drinking behavior (intention to drink, lifetime, past-month, and binge drinking) by conducting an ordinal regression analysis. The results show that both shared identities and joint participation were associated with all stages of drinking, controlling for friends' influence. Additionally, shared identification overlapped with joint participation was associated with more frequent drinking. Related policy implications are discussed.

We assess the potential of network motif profiles to characterize ego-networks in much the same way that a bag-of-words strategy allows text documents to be compared in a vector space framework. This is potentially valuable as a generic strategy for comparing nodes in a network in terms of the network structure in which they are embedded. In this paper, we consider the computational challenges and model selection decisions involved in network motif profiling. We also present three case studies concerning the analysis of Wikipedia edit networks, YouTube spam campaigns, and peer-to-peer lending in the Prosper marketplace.

This paper presents a variable neighborhood search (VNS) algorithm that is specially designed for the blockmodeling of two-mode binary network matrices in accordance with structural equivalence. Computational results for 768 synthetic test networks revealed that the VNS heuristic outperformed a relocation heuristic (RH) and a tabu search (TS) method for the same problem. Next, the three heuristics were applied to two-mode network data pertaining to the votes of member countries on resolutions in the United Nations General Assembly. A comparative analysis revealed that the VNS heuristic often provided slightly better criterion function values than RH and TS, and that these small differences in criterion function values could sometimes be associated with substantial differences in the actual partitions obtained. Overall, the results suggest that the VNS heuristic is a promising approach for blockmodeling of two-mode binary networks. Recommendations for extensions to stochastic blockmodeling applications are provided.

Degree distribution is a fundamental property of networks. While mean degree provides a standard measure of scale, there are several commonly used shape measures. Widespread use of a single shape measure would enable comparisons between networks and facilitate investigations about the relationship between degree distribution properties and other network features. This paper describes five candidate measures of heterogeneity and recommends the Gini coefficient. It has theoretical advantages over many of the previously proposed measures, is meaningful for the broad range of distribution shapes seen in different types of networks, and has several accessible interpretations. While this paper focuses on degree, the distribution of other node-based network properties could also be described with Gini coefficients.

We discuss our outreach efforts to introduce school students to network science and explain why researchers who study networks should be involved in such outreach activities. We provide overviews of modules that we have designed for these efforts, comment on our successes and failures, and illustrate the potentially enormous impact of such outreach efforts.

Does the human brain have a central connective core, and, if so, how costly is it?

Noninvasive imaging data allow the construction of network maps of the human brain, recording its structural and functional connectivity. A number of studies have reported on various characteristic network attributes, such as a tendency toward local clustering, high global efficiency, the prevalence of specific network motifs, and a pronounced community structure with several anatomically and functionally defined modules and interconnecting hub regions (Bullmore & Sporns, 2009; van den Heuvel & Hulshoff Pol, 2010; Sporns, 2011). Hubs are of particular interest in studies of the brain since they may play crucial roles in integrative processes and global brain communication, thought to be essential for many aspects of higher brain function. Indeed, hubs have been shown to correspond to brain regions that exhibit complex physiological responses and maintain widespread and diverse connection profiles with other parts of the brain. We asked if, in addition to being highly connected, brain hubs would also exhibit a strong tendency to be mutually interconnected, forming what has been called a “rich club” (Colizza et al., 2006). Rich club organization is present in a network if sets of high-degree nodes exhibit denser mutual connections than predicted on the basis of the degree sequence alone. We investigated rich club organization in the human brain in datasets that recorded weighted projections among different anatomical regions of the cerebral cortex, recorded from several cohorts of healthy human volunteers (van den Heuvel & Sporns, 2011; van den Heuvel et al., 2012).

The supplemental online material accompanying this article was not the final version. The final version is now published online. The publisher regrets the error.