Bilinear Mixed-Effects Models for Dyadic Data

Hoff, Peter D.

doi:10.1198/016214504000001015

Cited by 317 publications

(364 citation statements)

References 21 publications

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“…We apply an existing statistical SNA framework (13) known as latent space modeling (7,8) to analyze eight empirical trophic food webs that have been previously studied (14). These webs were observed from Goose Creek Bay in the St. Marks National Wildlife Refuge in the southeastern United States (15), the Benguela marine ecosystem off the South African coast (16), the grasslands in England and Wales (17), the Caribbean coral reefs with a reduced set of nodes (18), the northeast US continental shelf (19), a pond on Skipwith Common in England (20), the Caribbean island of St. Martin (21), and Ythan Estuary in Scotland (22).…”

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confidence: 99%

A unifying approach for food webs, phylogeny, social networks, and statistics

Chiu

Westveld²

2011

Proc. Natl. Acad. Sci. U.S.A.

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A food web consists of nodes, each consisting of one or more species. The role of each node as predator or prey determines the trophic relations that weave the web. Much effort in trophic food web research is given to understand the connectivity structure, or the nature and degree of dependence among nodes. Social network analysis (SNA) techniques—quantitative methods commonly used in the social sciences to understand network relational structure—have been used for this purpose, although postanalysis effort or biological theory is still required to determine what natural factors contribute to the feeding behavior. Thus, a conventional SNA alone provides limited insight into trophic structure. Here we show that by using novel statistical modeling methodologies to express network links as the random response of within- and internode characteristics (predictors), we gain a much deeper understanding of food web structure and its contributing factors through a unified statistical SNA. We do so for eight empirical food webs: Phylogeny is shown to have nontrivial influence on trophic relations in many webs, and for each web trophic clustering based on feeding activity and on feeding preference can differ substantially. These and other conclusions about network features are purely empirical, based entirely on observed network attributes while accounting for biological information built directly into the model. Thus, statistical SNA techniques, through statistical inference for feeding activity and preference, provide an alternative perspective of trophic clustering to yield comprehensive insight into food web structure.

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confidence: 99%

A unifying approach for food webs, phylogeny, social networks, and statistics

Chiu

Westveld²

2011

Proc. Natl. Acad. Sci. U.S.A.

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“…This formulation of latent space is inherently symmetrical 64 . Previously, we have shown that this latent space model accurately predicts cactus bug movements 21 .…”

Section: Methodsmentioning

confidence: 99%

“…where K is the number of dimensions in the Euclidean latent space (we set K ¼ 2) 63,64 . This formulation of latent space is inherently symmetrical 64 .…”

Section: Methodsmentioning

confidence: 99%

Network modularity reveals critical scales for connectivity in ecology and evolution

et al. 2013

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For nearly a century, biologists have emphasized the profound importance of spatial scale for ecology, evolution and conservation. Nonetheless, objectively identifying critical scales has proven incredibly challenging. Here we extend new techniques from physics and social sciences that estimate modularity on networks to identify critical scales for movement and gene flow in animals. Using four species that vary widely in dispersal ability and include both mark-recapture and population genetic data, we identify significant modularity in three species, two of which cannot be explained by geographic distance alone. Importantly, the inclusion of modularity in connectivity and population viability assessments alters conclusions regarding patch importance to connectivity and suggests higher metapopulation viability than when ignoring this hidden spatial scale. We argue that network modularity reveals critical meso-scales that are probably common in populations, providing a powerful means of identifying fundamental scales for biology and for conservation strategies aimed at recovering imperilled species.

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“…If a threshold function is used instead, the graph model corresponds to the random geometric graph model discussed in the next section. Extensions of this model can be found in [11,15,24].…”

Section: Spatial Models With Network-based Link Formationmentioning

confidence: 99%

Spatial Models for Virtual Networks

Janssen

2010

Programs, Proofs, Processes

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Abstract. This paper discusses the use of spatial graph models for the analysis of networks that do not have a direct spatial reality, such as web graphs, on-line social networks, or citation graphs. In a spatial graph model, nodes are embedded in a metric space, and link formation depends on the relative position of nodes in the space. It is argued that spatial models form a good basis for link mining: assuming a spatial model, the link information can be used to infer the spatial position of the nodes, and this information can then be used for clustering and recognition of node similarity. This paper gives a survey of spatial graph models, and discusses their suitability for link mining.

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Bilinear Mixed-Effects Models for Dyadic Data

Cited by 317 publications

References 21 publications

A unifying approach for food webs, phylogeny, social networks, and statistics

A unifying approach for food webs, phylogeny, social networks, and statistics

Network modularity reveals critical scales for connectivity in ecology and evolution

Spatial Models for Virtual Networks

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