Relational hyperevent models for polyadic interaction networks

Polyadic, or "multicast" social interaction networks arise when one sender addresses multiple receivers simultaneously. Currently available relational event models (REM) are not well suited to the analysis of polyadic interaction networks because they specify event rates for sets of receivers as functions of dyadic covariates associated with the sender and one receiver at a time. Relational hyperevent models (RHEM) address this problem by specifying event rates as functions of hyperedge covariates associated with the sender and the entire set of receivers. For instance, hyperedge covariates can express the tendency of senders to repeatedly address the same pairs (or larger sets) of receivers - a simple and frequent pattern in polyadic interaction data which, however, cannot be expressed with dyadic covariates. In this article we demonstrate the potential benefits of RHEMs for the analysis of polyadic social interaction. We define and discuss practically relevant effects that are not available for REMs but may be incorporated in empirical specifications of RHEM. We illustrate the empirical value of RHEM, and compare them with related REM, in a reanalysis of the canonical Enron email data

A Microstructural Approach to Self-Organizing:The Emergence of Attention Networks

A recent line of inquiry investigates new forms of organizing as bundles of novel solutions to universal problems of resource allocation and coordination: how to allocate organizational problems to organizational participants and how to integrate participants' resulting efforts. We contribute to this line of inquiry by reframing organizational attention as the outcome of a concatenation of self-organizing, microstructural mechanisms linking multiple participants to multiple problems, thus giving rise to an emergent attention network. We argue that, when managerial hierarchies are absent and authority is decentralized, observable acts of attention allocation produce interpretable signals that help participants to direct their attention and share information on how to coordinate and integrate their individual efforts. We theorize that the observed structure of an organizational attention network is generated by the concatenation of four interdependent micromechanisms: focusing, reinforcing, mixing, and clustering. In a statistical analysis of organizational problem solving within a large opensource software project, we find support for our hypotheses about the self-organizing dynamics of the observed attention network connecting organizational problems (software bugs) to organizational participants (volunteer contributors). We discuss the implications of attention networks for theory and practice by emphasizing the self-organizing character of organizational problem solving. We discuss the generalizability of our theory to a wider set of organizations in which participants can freely allocate their attention to problems and the outcomes of their allocation are publicly observable without cost.</p

Modeling frequency and type of interaction in event networks

Longitudinal social networks are increasingly given by event data; i.e., data coding the time and type of interaction between social actors. Examples include networks stemming from computer-mediated communication, open collaboration in wikis, phone call data and interaction among political actors. In this paper, we propose a general model for networks of dyadic, typed events. We decompose the probability of events into two components: the first modeling the frequency of interaction and the second modeling the conditional event type, i. e., the quality of interaction, given that interaction takes place. While our main contribution is methodological, for illustration we apply our model to data about political cooperation and conficts collected with the Kansas Event Data System. Special emphasis is given to the fact that some explanatory variables affect the frequency of interaction while others rather determine the level of cooperativeness vs. hostility, if interaction takes place. Furthermore, we analyze if and how model components controlling for network dependencies affect findings on the effects of more traditional predictors such as geographic proximity or joint alliance membership. We argue that modeling the conditional event type is a valuable – and in some cases superior – alternative to previously proposed models for networks of typed events

Longitudinal analysis of personal networks : the case of Argentinean migrants in Spain

Premi a l'excel·lència investigadora. 2010This paper discusses and illustrates various approaches for the longitudinal analysis of personal networks (multilevel analysis, regression analysis, and SIENA). We combined the different types of analyses in a study of the changing personal networks of immigrants. Data were obtained from 25 Argentineans in Spain, who were interviewed twice in a two-year interval. Qualitative interviews were used to estimate the amount of measurement error and to isolate important predictors. Quantitative analyses showed that the persistence of ties was explained by tie strength, network density, and alters' country of origin and residence. Furthermore, transitivity appeared to be an important tendency, both for acquiring new contacts and for the relationships among alters. At the network level, immigrants' networks were remarkably stable in composition and structure despite the high turnover. Clustered graphs have been used to illustrate the results. The results are discussed in light of adaptation to the host society

Patrones de cambio de las redes personales de inmigrantes en Cataluña

Resumen A través del análisis de las redes personales de personas emigradas en Cataluña (España) en dos oleadas separadas entre un año y medio y dos años, se ha podido establecer un modelo general de cambio y una codificación de los factores que explican esos cambios a partir de los testimonios de sus protagonistas. Los colectivos estudiados han sido argentinos (n=25), dominicanos (n=16), marroquíes (n=13) y personas de Senegal y Gambia (n=13). Así, mientras especialmente la obtención de un empleo y la participación en cursos y actividades deportivas representan cambios en la dirección del modelo general (evolución), el matrimonio y el nacimiento de hijos, la pertenencia a asociaciones étnicas, los viajes y las visitas de familiares y amigos representan cambios en la dirección inversa (involución). Gracias al uso de metarepresentaciones de esas redes personales ha sido posible comparar las redes de una misma persona en las dos oleadas y, al mismo tiempo, agregar y comparar las redes por colectivos, manifestándose diferencias en las modalidades de adaptación y cambio entre los diferentes colectivos. Abstract By analyzing two waves of personal networks of immigrants to Catalonia (Spain) with a period of 1.5-2 years in between, a general model of change resulting from migration is proposed and people explanations that account for the observed changes are coded. The analyzed communities are Argentineans (n=25), Dominicans (n=16), Moroccans (n=13), and persons from Senegal and Gambia (n=13). It turns out that getting a job and the participation in courses and team sport pushes for change that follows the proposed general model (evolution). In contrast, marrying a partner of the same origin, getting children, participating in an ethnic association, traveling to the country of origin, and being visited by kin and friends frequently leads to change in the opposite direction (involution). The use of meta-visualizations of personal networks facilitates the comparison of networks of the same individual at different time points, as well as the comparison of networks aggregated over different communities. This aggregation shows that each of the four communities exhibits different ways of adaptation and chang

Dynamic network analysis of contact diaries

Analyzing two-mode networks linking actors to events they attend may help to uncover the structure and evolution of social networks. This classic social network insight is particularly valuable in the analysis of data extracted from contact diaries where contact events produce — and at the same time are the product of relations among participants. Contact events may comprise any number of actors meeting at a specific point in time. In this paper we recall the correspondence between two-mode actor–event networks and hypergraphs, and propose relational hyperevent models (RHEM) as a general modeling framework for networks of time-stamped multi-actor events in which the diarist (“ego”) simultaneously meets several of her alters. RHEM can estimate event intensities associated with each possible subset of actors that may jointly participate in events, and test network effects that may be of theoretical or empirical interest. Examples of such effects include preferential attachment, prior shared activity (familiarity), closure, and covariate effects explaining the propensity of actors to co-attend events. Statistical tests of these effects can uncover processes that govern the formation and evolution of informal groups among the diarist’s alters. We illustrate the empirical value of RHEM using data comprising almost 2000 meeting events of former British Prime Minister Margaret Thatcher with her cabinet ministers, transcribed from contact diaries covering her first term in office (1979–1983)

Recognizing Modes of Acculturation in Personal Networks of Migrants

An individual's personal network encodes social contacts as well as relations among them. Personal networks are therefore considered to be characteristic and meaningful variables of individuals-supplementing more traditional characteristics such as age, gender, race, or job position. We analyze an ensemble of several hundred personal networks of migrants using a recently introduced classification method. As a result, individuals are partitioned into groups defined by similarity of their personal networks, and abstract summaries of classes are obtained. From the analysis we can conclude that Berry's modes of acculturation feature prominently in the empirical data

Sub-millimeter Observations of Giant Molecular Clouds in the Large Magellanic Cloud: Temperature and Density as Determined from J=3-2 and J=1-0 transitions of CO

We have carried out sub-mm 12CO(J=3-2) observations of 6 giant molecular clouds (GMCs) in the Large Magellanic Cloud (LMC) with the ASTE 10m sub-mm telescope at a spatial resolution of 5 pc and very high sensitivity. We have identified 32 molecular clumps in the GMCs and revealed significant details of the warm and dense molecular gas with n(H2) $\sim$ 10$^{3-5}$ cm$^{-3}$ and Tkin $\sim$ 60 K. These data are combined with 12CO(J=1-0) and 13CO(J=1-0) results and compared with LVG calculations. We found that the ratio of 12CO(J=3-2) to 12CO(J=1-0) emission is sensitive to and is well correlated with the local Halpha flux. We interpret that differences of clump propeties represent an evolutionary sequence of GMCs in terms of density increase leading to star formation.Type I and II GMCs (starless GMCs and GMCs with HII regions only, respectively) are at the young phase of star formation where density does not yet become high enough to show active star formation and Type III GMCs (GMCs with HII regions and young star clusters) represents the later phase where the average density is increased and the GMCs are forming massive stars. The high kinetic temperature correlated with \Halpha flux suggests that FUV heating is dominant in the molecular gas of the LMC.Comment: 74 pages, including 41 figures, accepted for publication in ApJ