skip to main content
US FlagAn official website of the United States government
dot gov icon
Official websites use .gov
A .gov website belongs to an official government organization in the United States.
https lock icon
Secure .gov websites use HTTPS
A lock ( lock ) or https:// means you've safely connected to the .gov website. Share sensitive information only on official, secure websites.

Explore Research Products in the PAR
It may take a few hours for recently added research products to appear in PAR search results.


Title: backbone: An R package to extract network backbones
Networks are useful for representing phenomena in a broad range of domains. Although their ability to represent complexity can be a virtue, it is sometimes useful to focus on a simplified network that contains only the most important edges: the backbone. This paper introduces and demonstrates a substantially expanded version of the backbone package for R, which now provides methods for extracting backbones from weighted networks, weighted bipartite projections, and unweighted networks. For each type of network, fully replicable code is presented first for small toy examples, then for complete empirical examples using transportation, political, and social networks. The paper also demonstrates the implications of several issues of statistical inference that arise in backbone extraction. It concludes by briefly reviewing existing applications of backbone extraction using the backbone package, and future directions for research on network backbone extraction.  more » « less
Award ID(s):
2016320
PAR ID:
10338882
Author(s) / Creator(s):
Editor(s):
Cherifi, Hocine
Date Published:
Journal Name:
PLOS ONE
Volume:
17
Issue:
5
ISSN:
1932-6203
Page Range / eLocation ID:
e0269137
Format(s):
Medium: X
Sponsoring Org:
National Science Foundation
More Like this
  1. ; ; (, PLOS ONE)
    Rozenblat, Celine (Ed.)
    Bipartite projections are used in a wide range of network contexts including politics (bill co-sponsorship), genetics (gene co-expression), economics (executive board co-membership), and innovation (patent co-authorship). However, because bipartite projections are always weighted graphs, which are inherently challenging to analyze and visualize, it is often useful to examine the ‘backbone,’ an unweighted subgraph containing only the most significant edges. In this paper, we introduce the R package backbone for extracting the backbone of weighted bipartite projections, and use bill sponsorship data from the 114 th session of the United States Senate to demonstrate its functionality. 
    more » « less
  2. (, Connections)
    Abstract Political network data can often be challenging to collect and clean for analysis. This article demonstrates how the incidentally and backbone packages for R can be used together to construct networks among legislators in the US Congress. These networks can be customized to focus on a specific chamber (Senate or House of Representatives), session (2003 to present), legislation type (bills and resolutions), and policy area (32 topics). Four detailed examples with replicable code are presented to illustrate the types of networks and types of insights that can be obtained using these tools. 
    more » « less
  3. ; (, PLOS ONE)
    Papadopoulos, Fragkiskos (Ed.)
    Bipartite projections (e.g., event co-attendance) are often used to measure unipartite networks of interest (e.g., social interaction). Backbone extraction models can be useful for reducing the noise inherent in bipartite projections. However, these models typically assume that the bipartite edges (e.g., who attended which event) are unconstrained, which may not be true in practice (e.g., a person cannot attend an event held prior to their birth). We illustrate the importance of correctly modeling such edge constraints when extracting backbones, using both synthetic data that varies the number and type of constraints, and empirical data on children’s play groups. We find that failing to impose relevant constraints when the data contain constrained edges can result in the extraction of an inaccurate backbone. Therefore, we recommend that when bipartite data contain constrained edges, backbones be extracted using a model such as the Stochastic Degree Sequence Model with Edge Constraints (SDSM-EC). 
    more » « less
  4. ; ; (, 2023 5th International Conference on Bio-engineering for Smart Technologies (BioSMART))
    The concept of stimulus feature tuning isfundamental to neuroscience. Cortical neurons acquire their feature-tuning properties by learning from experience and using proxy signs of tentative features’ potential usefulness that come from the spatial and/or temporal context in which these features occur. According to this idea, local but ultimately behaviorally useful features should be the ones that are predictably related to other such features either preceding them in time or taking place side-by-side with them. Inspired by this idea, in this paper, deep neural networks are combined with Canonical Correlation Analysis (CCA) for feature extraction and the power of the features is demonstrated using unsupervised cross-modal prediction tasks. CCA is a multi-view feature extraction method that finds correlated features across multiple datasets (usually referred to as views or modalities). CCA finds linear transformations of each view such that the extracted principal components, or features, have a maximal mutual correlation. CCA is a linear method, and the features are computed by a weighted sum of each view's variables. Once the weights are learned, CCA can be applied to new examples and used for cross-modal prediction by inferring the target-view features of an example from its given variables in a source (query) view. To test the proposed method, it was applied to the unstructured CIFAR-100 dataset of 60,000 images categorized into 100 classes, which are further grouped into 20 superclasses and used to demonstrate the mining of image-tag correlations. CCA was performed on the outputs of three pre-trained CNNs: AlexNet, ResNet, and VGG. Taking advantage of the mutually correlated features extracted with CCA, a search for nearest neighbors was performed in the canonical subspace common to both the query and the target views to retrieve the most matching examples in the target view, which successfully predicted the superclass membership of the tested views without any supervised training. 
    more » « less
  5. ; ; (, Biometrika)
    null (Ed.)
    Summary While many statistical models and methods are now available for network analysis, resampling of network data remains a challenging problem. Cross-validation is a useful general tool for model selection and parameter tuning, but it is not directly applicable to networks since splitting network nodes into groups requires deleting edges and destroys some of the network structure. In this paper we propose a new network resampling strategy, based on splitting node pairs rather than nodes, that is applicable to cross-validation for a wide range of network model selection tasks. We provide theoretical justification for our method in a general setting and examples of how the method can be used in specific network model selection and parameter tuning tasks. Numerical results on simulated networks and on a statisticians’ citation network show that the proposed cross-validation approach works well for model selection. 
    more » « less
  • Citation Formats
  • MLA
  • APA
  • Chicago
  • BibTeX
  • Save / Share this Record
  • Send to Email