Social Network Analysis

Wasserman, Stanley; Faust, Katherine

doi:10.1017/cbo9780511815478

Cited by 16,041 publications

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“…We did not set networks as scale free since recent primate studies showed that social networks were not scale free (i.e. with a power distribution of links) but random (see Flack et al, 2006;McCowan et al, 2008;Sueur and Petit, 2008;Kasper and Voelkl, 2009;Ramos Fernandez et al, 2009 for studies on primate social networks; see Wasserman and Faust, 1994 for social network theory). We can observe on Fig.…”

Section: Theoretical Datamentioning

confidence: 99%

“…Illustration of a social network with 40 individuals (squares). Number labels indicated the number of group members by which an individual is groomed (indegree, Wasserman and Faust (1994). We showed from this graph that even if the number of partners groomed by the same individual was fixed at 5, an individual can be groomed by more (or less) than 5 grooming partners in the model.…”

Section: Design Conceptsmentioning

confidence: 99%

“…Group structure was analyzed using the social networking approach (Krause et al, 2007;Wasserman and Faust, 1994;Wey et al, 2008;Whitehead 2009). We used two indexes to determine a group structure.…”

Section: Group Structure Analysesmentioning

confidence: 99%

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Group size, grooming and fission in primates: A modeling approach based on group structure

Sueur

Deneubourg²,

Petit

et al. 2011

Journal of Theoretical Biology

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a b s t r a c tIn social animals, fission is a common mode of group proliferation and dispersion and may be affected by genetic or other social factors. Sociality implies preserving relationships between group members. An increase in group size and/or in competition for food within the group can result in decrease certain social interactions between members, and the group may split irreversibly as a consequence. One individual may try to maintain bonds with a maximum of group members in order to keep group cohesion, i.e. proximity and stable relationships. However, this strategy needs time and time is often limited. In addition, previous studies have shown that whatever the group size, an individual interacts only with certain grooming partners. There, we develop a computational model to assess how dynamics of group cohesion are related to group size and to the structure of grooming relationships. Groups' sizes after simulated fission are compared to observed sizes of 40 groups of primates. Results showed that the relationship between grooming time and group size is dependent on how each individual attributes grooming time to its social partners, i.e. grooming a few number of preferred partners or grooming equally or not all partners. The number of partners seemed to be more important for the group cohesion than the grooming time itself. This structural constraint has important consequences on group sociality, as it gives the possibility of competition for grooming partners, attraction for high ranking individuals as found in primates' groups. It could, however, also have implications when considering the cognitive capacities of primates.

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Section: Theoretical Datamentioning

confidence: 99%

Section: Design Conceptsmentioning

confidence: 99%

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Group size, grooming and fission in primates: A modeling approach based on group structure

Sueur

Deneubourg²,

Petit

et al. 2011

Journal of Theoretical Biology

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“…and not so strongly with those in the other communities: See [12,23,26,29,36] for a discussion of relevant definitions.…”

Section: Network and Community Structuresmentioning

confidence: 99%

Checking the reliability of a linear-programming based approach towards detecting community structures in networks

Chen

Dress²,

2007

IET Systems Biology

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“…In this paper, a statistical approach is employed, originating from social network analysis field [11]. It is important to recognize that biological processes underlying the network data as well as experimental measurements are of stochastic nature: some of the interactions might be unreliable and some of the real relations might be missed or not measured.…”

Section: Introductionmentioning

confidence: 99%

Bayesian statistical modelling of human protein interaction network incorporating protein disorder information

Bulashevska

Eils

2010

BMC Bioinformatics

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BackgroundWe present a statistical method of analysis of biological networks based on the exponential random graph model, namely p2-model, as opposed to previous descriptive approaches. The model is capable to capture generic and structural properties of a network as emergent from local interdependencies and uses a limited number of parameters. Here, we consider one global parameter capturing the density of edges in the network, and local parameters representing each node's contribution to the formation of edges in the network. The modelling suggests a novel definition of important nodes in the network, namely social, as revealed based on the local sociality parameters of the model. Moreover, the sociality parameters help to reveal organizational principles of the network. An inherent advantage of our approach is the possibility of hypotheses testing: a priori knowledge about biological properties of the nodes can be incorporated into the statistical model to investigate its influence on the structure of the network.ResultsWe applied the statistical modelling to the human protein interaction network obtained with Y2H experiments. Bayesian approach for the estimation of the parameters was employed. We deduced social proteins, essential for the formation of the network, while incorporating into the model information on protein disorder. Intrinsically disordered are proteins which lack a well-defined three-dimensional structure under physiological conditions. We predicted the fold group (ordered or disordered) of proteins in the network from their primary sequences. The network analysis indicated that protein disorder has a positive effect on the connectivity of proteins in the network, but do not fully explains the interactivity.ConclusionsThe approach opens a perspective to study effects of biological properties of individual entities on the structure of biological networks.

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Social Network Analysis

Cited by 16,041 publications

References 0 publications

Group size, grooming and fission in primates: A modeling approach based on group structure

Group size, grooming and fission in primates: A modeling approach based on group structure

Checking the reliability of a linear-programming based approach towards detecting community structures in networks

Bayesian statistical modelling of human protein interaction network incorporating protein disorder information

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