Leadership in neurology: a social network analysis.

OBJECTIVE: Two important leadership posts in American neurology are the presidents of the American Academy of Neurology (AAN) and the American Neurological Association (ANA). In this article, we use social network analysis, based on graph theory, to map the professional ties of presidents of the AAN and ANA since 1948. We examined whether institution ranking was related to being president of either organization, and whether there were core groups of presidents, institutions of employment during presidency, or training programs (residency and fellowship) in the combined and separate AAN and ANA networks. METHODS: Using archival data, we constructed a series of relational tables of the presidents and their affiliations. We used a chi-square analysis to test the relation between institution ranking and organization affiliation. For network data, we used a 2-mode analysis with measures of node, dyad, and network characteristics. RESULTS: ANA presidents were more likely to be employed at ranked institutions compared to AAN presidents. Ten presidents bridged both organizations, and therefore had the highest centrality in the combined network. Presidents trained in a core group of similar residency and fellowship programs that included Harvard, Columbia, Cornell, and Mayo Clinic for AAN presidents, and Harvard, Columbia, Yale, and University College London for ANA presidents. In contrast, during their presidency, AAN and ANA presidents worked at a diffuse set of institutions without a core group. INTERPRETATION: Training programs are leadership hubs, and should be targeted to develop future presidents and influence trends in the neurology leadership network.

Social network analysis of wild chimpanzees provides insights for predicting infectious disease risk.

1. Heterogeneity in host association patterns can alter pathogen transmission and strategies for control. Great apes are highly social and endangered animals that have experienced substantial population declines from directly transmitted pathogens; as such, network approaches to quantify contact heterogeneity could be crucially important for predicting infection probability and outbreak size following pathogen introduction, especially owing to challenges in collecting real-time infection data for endangered wildlife. 2. We present here the first study using network analysis to quantify contact heterogeneity in wild apes, with applications for predicting community-wide infectious disease risk. Specifically, within a wild chimpanzee community, we ask how associations between individuals vary over time, and we identify traits of highly connected individuals that might contribute disproportionately to pathogen spread. 3. We used field observations of behavioural encounters in a habituated wild chimpanzee community in Kibale National Park, Uganda to construct monthly party level (i.e. subgroup) and close-contact (i.e. ≤ 5 m) association networks over a 9-month period. 4. Network analysis revealed that networks were highly dynamic over time. In particular, oestrous events significantly increased pairwise party associations, suggesting that community-wide disease outbreaks should be more likely to occur when many females are in oestrus. 5. Bayesian models and permutation tests identified traits of chimpanzees that were highly connected within the network. Individuals with large families (i.e. mothers and their juveniles) that range in the core of the community territory and to a lesser extent high-ranking males were central to association networks, and thus represent the most important individuals to target for disease intervention strategies. 6. Overall, we show striking temporal variation in network structure and traits that predict association patterns in a wild chimpanzee community. These empirically-derived networks can inform dynamic models of pathogen transmission and have practical applications for infectious disease management of endangered wildlife species.

A social network approach to demonstrate the diffusion and change process of intervention from peer health advocates to the drug using community.

Project RAP (Risk Avoidance Partnership) trained 112 active drug users to become peer health advocates (PHAs). Six months after baseline survey (N(bl) = 522), 91.6% of PHAs and 56.6% of community drug users adopted the RAP innovation of giving peer intervention, and 59.5% of all participants (N(6m) = 367) were exposed to RAP innovation. Sociometric network analysis shows that adoption of and exposure to RAP innovation was associated with proximity to a PHA or a highly active interventionist (HAI), being directly linked to multiple PHAs/HAIs, and being located in a network sector where multiple PHAs/HAIs were clustered. RAP innovation has diffused into the Hartford drug-using community.

Network analysis in the social sciences.

Over the past decade, there has been an explosion of interest in network research across the physical and social sciences. For social scientists, the theory of networks has been a gold mine, yielding explanations for social phenomena in a wide variety of disciplines from psychology to economics. Here, we review the kinds of things that social scientists have tried to explain using social network analysis and provide a nutshell description of the basic assumptions, goals, and explanatory mechanisms prevalent in the field. We hope to contribute to a dialogue among researchers from across the physical and social sciences who share a common interest in understanding the antecedents and consequences of network phenomena.

Percolation theory applied to measures of fragmentation in social networks.

We apply percolation theory to a recently proposed measure of fragmentation F for social networks. The measure F is defined as the ratio between the number of pairs of nodes that are not connected in the fragmented network after removing a fraction q of nodes and the total number of pairs in the original fully connected network. We compare F with the traditional measure used in percolation theory, P(infinity), the fraction of nodes in the largest cluster relative to the total number of nodes. Using both analytical and numerical methods from percolation, we study Erdos-Rényi and scale-free networks under various types of node removal strategies. The removal strategies are random removal, high degree removal, and high betweenness centrality removal. We find that for a network obtained after removal (all strategies) of a fraction q of nodes above percolation threshold, P(infinity) approximately (1-F)1/2. For fixed P(infinity) and close to percolation threshold (q=qc), we show that 1-F better reflects the actual fragmentation. Close to qc, for a given P(infinity), 1-F has a broad distribution and it is thus possible to improve the fragmentation of the network. We also study and compare the fragmentation measure F and the percolation measure P(infinity) for a real social network of workplaces linked by the households of the employees and find similar results.

Defining and measuring trophic role similarity in food webs using regular equivalence.

We present a graph theoretic model of analysing food web structure called regular equivalence. Regular equivalence is a method for partitioning the species in a food web into "isotrophic classes" that play the same structural roles, even if they are not directly consuming the same prey or if they do not share the same predators. We contrast regular equivalence models, in which two species are members of the same trophic group if they have trophic links to the same set of other trophic groups, with structural equivalence models, in which species are equivalent if they are connected to the exact same other species. Here, the regular equivalence approach is applied to two published food webs: (1) a topological web (Malaysian pitcher plant insect food web) and (2) a carbon-flow web (St. Marks, Florida seagrass ecosystem food web). Regular equivalence produced a more satisfactory set of classes than did the structural approach, grouping basal taxa with other basal taxa and not with top predators. Regular equivalence models provide a way to mathematically formalize trophic position, trophic group and trophic niche. These models are part of a family of models that includes structural models used extensively by ecologists now. Regular equivalence models uncover similarities in trophic roles at a higher level of organization than do the structural models. The approach outlined is useful for measuring the trophic roles of species in food web models, measuring similarity in trophic relations of two or more species, comparing food webs over time and across geographic regions, and aggregating taxa into trophic groups that reduce the complexity of ecosystem feeding relations without obscuring network relationships. In addition, we hope the approach will prove useful in predicting the outcome of predator-prey interactions in experimental studies.