Comparing Alternatives to the Fixed Degree Sequence Model for Extracting the Backbone of Bipartite Projections

Abstract

Projections of bipartite or two-mode networks capture co-occurrences, and are used in diverse fields (e.g., ecology, economics, bibliometrics, politics) to represent unipartite networks. A key challenge in analyzing such networks is determining whether an observed number of co-occurrences between two nodes is significant, and therefore whether an edge exists between them. One approach, the fixed degree sequence model (FDSM), evaluates the significance of an edge's weight by comparison to a null model in which the degree sequences of the original bipartite network are fixed. Although the FDSM is an intuitive null model, it is computationally expensive because it requires Monte Carlo simulation to estimate each edge's $p$-value, and therefore is impractical for large projections. In this paper, we explore four potential alternatives to FDSM: fixed fill model (FFM), fixed row model (FRM), fixed column model (FCM), and stochastic degree sequence model (SDSM). We compare these models to FDSM in terms of accuracy, speed, statistical power, similarity, and ability to recover known communities. We find that the computationally-fast SDSM offers a statistically conservative but close approximation of the computationally-impractical FDSM under a wide range of conditions, and that it correctly recovers a known community structure even when the signal is weak. Therefore, although each backbone model may have particular applications, we recommend SDSM for extracting the backbone of bipartite projections when FDSM is impractical.

References in corpus (23)

• Finding and evaluating community structure in networks
• Why social networks are different from other types of networks
• Extracting the multiscale backbone of complex weighted networks
• Scale-free networks are rare
• Module identification in bipartite and directed networks
• Statistically validated networks in bipartite complex systems
• The Babe Ruth Algorithm: a fast, unbiased procedure to randomize presence-absence data matrices with fixed row and column totals
• Randomizing bipartite networks: the case of the World Trade Web
• Equivalence and nonequivalence of ensembles: Thermodynamic, macrostate, and measure levels
• Inferring monopartite projections of bipartite networks: an entropy-based approach
• Breaking of ensemble equivalence in networks
• Unwinding the hairball graph: pruning algorithms for weighted complex networks
• Urbanization and Economic Complexity
• backbone: An R Package for extracting the backbone of bipartite projections
• Detecting coalitions by optimally partitioning signed networks of political collaboration
• Transitivity and degree assortativity explained: The bipartite structure of social networks
• Grand canonical validation of the bipartite International Trade Network
• Meta-validation of bipartite network projections
• The ambiguity of nestedness under soft and hard constraints
• Ensemble inequivalence in random graphs
• Fast extraction of the backbone of projected bipartite networks to aid community detection
• Identifying hidden coalitions in the US House of Representatives by optimally partitioning signed networks based on generalized balance
• Entropy-based randomisation of rating networks

Cited by in corpus (11)

• backbone: An R package to extract network backbones
• An Evaluation Tool for Backbone Extraction Techniques in Weighted Complex Networks
• Bow-Tie Structures of Twitter Discursive Communities
• Proper network randomization is key to assessing social balance
• Pattern detection in bipartite networks: a review of terminology, applications and methods
• fastball: A fast algorithm to sample bipartite graphs with fixed degree sequences
• Entropy-based models to randomize real-world hypergraphs
• Patterns of link reciprocity in directed, signed networks
• Stochastic Degree Sequence Model with Edge Constraints (SDSM-EC) for Backbone Extraction
• When can networks be inferred from observed groups?
• A multilevel network approach to revealing patterns of online political selective exposure