Bayesian inference of network structure from unreliable data

Young, Jean-Gabriel; Cantwell, George T.; Newman, M. E. J.

doi:10.1093/comnet/cnaa046

Cited by 62 publications

(74 citation statements)

References 53 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…Some practitioners may opt to selectively remove Thresholding can induce biases and confounds (Zalesky, Fornito et al 2012) in the overall network topology and therefore must be performed with justification and with an understanding that different thresholds could possibly affect the investigation's main findings. Alternatively, analytical approaches that incorporate noisy edges or imperfect graph observation could be a fruitful future direction for network neuroscience (Young, Cantwell et al 2020).…”

Section: Saenger Et Al 2014)mentioning

confidence: 99%

Edges in brain networks: Contributions to models of structure and function

Faskowitz

Betzel

Sporns

2021

Network Neuroscience

View full text Add to dashboard Cite

Network models describe the brain as sets of nodes and edges that represent its distributed organization. So far, most discoveries in network neuroscience have prioritized insights that highlight distinct groupings and specialized functional contributions of network nodes. Importantly, these functional contributions are determined and expressed by the web of their interrelationships, formed by network edges. Here, we underscore the important contributions made by brain network edges for understanding distributed brain organization. Different types of edges represent different types of relationships, including connectivity and similarity among nodes. Adopting a specific definition of edges can fundamentally alter how we analyze and interpret a brain network. Furthermore, edges can associate into collectives and higher-order arrangements, describe time series, and form edge communities that provide insights into brain network topology complementary to the traditional node-centric perspective. Focusing on the edges, and the higher-order or dynamic information they can provide, discloses previously underappreciated aspects of structural and functional network organization.

show abstract

Section: Saenger Et Al 2014)mentioning

confidence: 99%

Edges in brain networks: Contributions to models of structure and function

Faskowitz

Betzel

Sporns

2021

Network Neuroscience

View full text Add to dashboard Cite

show abstract

“…Bascompte et al 2003) and can provide informative priors for Bayesian inference of network structure (e.g. J.-G. Young, Cantwell, and Newman 2021). It is noteworthy that for this metaweb, the relevant information was extracted at the first rank.…”

Section: Results and Discussion Of The Case Studymentioning

confidence: 99%

Food web reconstruction through phylogenetic transfer of low-rank network representation

Strydom¹,

Bouskila²,

Banville³

et al. 2021

Preprint

View full text Add to dashboard Cite

Despite their importance in many ecological processes, collecting data and information on ecological interactions, and therefore species interaction networks, is an exceedingly challenging task. For this reason, large parts of the world have a deficit of data of which species interact, and what we can expect the network structure of these interactions to be. As data collection alone is unlikely to be sufficient at filling these global gaps, community ecologists must adopt predictive methods. In this contribution we develop such a method, relying on graph embedding (the extraction of explanatory latent variables from known graph structures) and transfer learning (the application of previous solution to novel problems with limited predictors overlap) in order to assemble a predicted list of trophic interactions between mammals of Canada. This interaction list is derived from extensive knowledge of the mammalian food web of Europe, despite the fact that there are fewer than 5% of common species between the two locations. We provide guidance on how this method can be adapted by substituting some approaches or predictors in order to make it more generally applicable to a broad family of ecological problems.

show abstract

“…More complex methods have the potential to better account for the biases and noisiness we have explored here. This may be through assessing the "credibility" of reporters (An & Schramski, 2015) or by using Bayesian approaches to assess consensus or disagreement about the existence or strength of particular ties (Butts, 2003;Newman, 2018;Young & Newman, 2021). We note, however, that given the subjectivity involved in the perception of relationships, these methods do not necessarily recover an objective "groundtruth."…”

Section: Discussionmentioning

confidence: 99%

“…The simplest techniques involve considering the two networks in isolation (Koster, 2018;Simpson, 2020), or combining them, either by taking the union (i.e., including all reported ties) (Nolin, 2010; or, opposingly, taking the intersection (i.e., taking only concordant ties) (Krackhardt & Kilduff, 1990). Other techniques try to assess and then account for differences in each informant's accuracy, as with cultural consensus approaches (Romney & Weller, 1984;An & Schramski, 2015), while others use a more explicitly inferential and Bayesian approach (Butts, 2003;Young & Newman, 2021). Aggregation techniques may also depend on whether networks are weighted or unweighted and how many sources of information are being integrated.…”

Section: Informant Accuracy and Double-samplingmentioning

confidence: 99%

Measuring reciprocity: Double sampling, concordance, and network construction

Ready¹

2021

Preprint

View full text Add to dashboard Cite

Reciprocity—the mutual provisioning of support/goods—is a pervasive feature of social life. Di- rected networks provide a way to examine the structure of reciprocity in a community. However, measuring social networks involves assumptions about what relationships matter and how to elicit them, which may impact observed reciprocity. In particular, the practice of aggregating multiple sources of data on the same relationship (e.g., “double-sampled” data, where both the “giver” and “re- ceiver” are asked to report on their relationship) may have pronounced impacts on network structure. To investigate these issues, we examine concordance (ties reported by both parties) and reciprocity in a set of directed, double-sampled social support networks. We find low concordance in people’s responses. Taking either the union (including any reported ties) or the intersection (including only concordant ties) of double-sampled relationships results in dramatically higher levels of reciprocity. Using multilevel exponential random graph models of social support networks from 75 villages in India, we show that these changes cannot be fully explained by the changes in density resulting from aggregation. Respondents’ tendency to name the same people as both givers and receivers of support plays an important role, but this tendency varies across contexts and relationships type. We argue that no single method of aggregation should necessarily be seen as the “correct” choice for aggregation of multiple sources of data on a single relationship type. Methods of aggregation should depend on the research question, the context, and the relationship in question.

show abstract

Bayesian inference of network structure from unreliable data

Cited by 62 publications

References 53 publications

Edges in brain networks: Contributions to models of structure and function

Edges in brain networks: Contributions to models of structure and function

Food web reconstruction through phylogenetic transfer of low-rank network representation

Measuring reciprocity: Double sampling, concordance, and network construction

Contact Info

Product

Resources

About