Testing biological network motif significance with exponential random graph models

Stivala, Alex; Lomi, Alessandro

doi:10.1007/s41109-021-00434-y

“…The model helped answer questions about the existence of patterns in the network including whether or not the network exhibited the characteristic of homophily and how organizations should understand their role in the network (Williams and Hristov (2018)). In the biological world, Stivala et al show that ERGM can address some of the limitations that previous research had found in modeling biological processes (Stivala and Lomi (2021)). These examples show the incredible flexibility and significance of exponential random graph models.…”

Section: Examples and Contextmentioning

confidence: 99%

Hierarchical Bayesian Adaptive Lasso Methods on Exponential Random Graph Models

Han,

Modisette,

Forthofer

et al. 2023

Preprint

0

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The analysis of network data has become an increasingly prominent and demanding field across multiple research fields including data science, health, and social sciences, requiring the development of robust models and efficient computational methods. One well-established and widely employed modeling approach for network data is the Exponential Random Graph Model (ERGM). Despite its popularity, there is a recognized necessity for further advancements to enhance its flexibility and variable selection capabilities. To address this need, we propose a novel hierarchical Bayesian adaptive lasso model (BALERGM), which builds upon the foundations of the ERGM. The BALERGM leverages the strengths of the ERGM and incorporates the flexible adaptive lasso technique, thereby facilitating effective variable selection and tackling the inherent challenges posed by high-dimensional network data. The model improvements have been assessed through the analysis of simulated data, as well as two authentic datasets. These datasets encompassed friendship networks and a respondent-driven sampling dataset on active and healthy lifestyle awareness programs.

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“…They are represented in Fig. 1 b. Triadic and tetradic connections are known as the building blocks of complex networks 45 , playing the role of functional modules or evolutionary signs in biological networks 46 , 47 , homophily-driven connections in social networks 48 , complementarity-driven structures in production networks 33 , 36 , their change in time being interpreted as self-organizing processes in the World Trade Web (WTW) 49 , 50 , and early-warning signals of topological collapse in inter-bank networks 51 , 52 and stock market networks 42 . It has been proven that for the majority of (available) real-world networks, the triadic structure is maximally random 53 and by fixing it their global structure is statistically determined 54 .…”

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confidence: 99%

Commodity-specific triads in the Dutch inter-industry production network

Di Vece,

Pijpers,

Garlaschelli

2024

Sci Rep

0

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Triadic motifs are the smallest building blocks of higher-order interactions in complex networks and can be detected as over-occurrences with respect to null models with only pair-wise interactions. Recently, the motif structure of production networks has attracted attention in light of its possible role in the propagation of economic shocks. However, its characterization at the level of individual commodities is still poorly understood. Here we analyze both binary and weighted triadic motifs in the Dutch inter-industry production network disaggregated at the level of 187 commodity groups, which Statistics Netherlands reconstructed from National Accounts registers, surveys and known empirical data. We introduce appropriate null models that filter out node heterogeneity and the strong effects of link reciprocity and find that, while the aggregate network that overlays all products is characterized by a multitude of triadic motifs, most single-product layers feature no significant motif, and roughly 85% of the layers feature only two motifs or less. This result paves the way for identifying a simple ‘triadic fingerprint’ of each commodity and for reconstructing most product-specific networks from partial information in a pairwise fashion by controlling for their reciprocity structure. We discuss how these results can help statistical bureaus identify fine-grained information in structural analyses of interest for policymakers.

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Hierarchical Bayesian adaptive lasso methods on exponential random graph models

Han,

Modisette,

Forthofer

et al. 2024

Appl Netw Sci

0

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The analysis of network data has become an increasingly prominent and demanding field across multiple research fields including data science, health, and social sciences, requiring the development of robust models and efficient computational methods. One well-established and widely employed modeling approach for network data is the Exponential Random Graph Model (ERGM). Despite its popularity, there is a recognized necessity for further advancements to enhance its flexibility and variable selection capabilities. To address this need, we propose a novel hierarchical Bayesian adaptive lasso model (BALERGM), which builds upon the foundations of the ERGM. The BALERGM leverages the strengths of the ERGM and incorporates the flexible adaptive lasso technique, thereby facilitating effective variable selection and tackling the inherent challenges posed by high-dimensional network data. The model improvements have been assessed through the analysis of simulated data, as well as two authentic datasets. These datasets encompassed friendship networks and a respondent-driven sampling dataset on active and healthy lifestyle awareness programs.

show abstract

Testing biological network motif significance with exponential random graph models

Cited by 6 publications

References 135 publications

Hierarchical Bayesian Adaptive Lasso Methods on Exponential Random Graph Models

Hierarchical Bayesian Adaptive Lasso Methods on Exponential Random Graph Models

Commodity-specific triads in the Dutch inter-industry production network

Hierarchical Bayesian adaptive lasso methods on exponential random graph models

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