Estimating network structure from unreliable measurements

Abstract

Most empirical studies of networks assume that the network data we are given represent a complete and accurate picture of the nodes and edges in the system of interest, but in real-world situations this is rarely the case. More often the data only specify the network structure imperfectly -- like data in essentially every other area of empirical science, network data are prone to measurement error and noise. At the same time, the data may be richer than simple network measurements, incorporating multiple measurements, weights, lengths or strengths of edges, node or edge labels, or annotations of various kinds. Here we develop a general method for making estimates of network structure and properties using any form of network data, simple or complex, when the data are unreliable, and give example applications to a selection of social and biological networks.

arXiv comment19 pages, 3 figures. Title changed in this version. Other minor updates and corrections

References in corpus (28)

• Emergence of scaling in random networks
• Discovering governing equations from data: Sparse identification of nonlinear dynamical systems
• Random graphs with arbitrary degree distributions and their applications
• Mixing patterns in networks
• The structure and dynamics of multilayer networks
• Classes of behavior of small-world networks
• ARACNE: An Algorithm for the Reconstruction of Gene Regulatory Networks in a Mammalian Cellular Context
• Hierarchical structure and the prediction of missing links in networks
• Stochastic blockmodels and community structure in networks
• Structure and tie strengths in mobile communication networks
• Scale-free topology of e-mail networks
• Missing and spurious interactions and the reconstruction of complex networks
• The physics of spreading processes in multilayer networks
• Measuring large-scale social networks with high resolution
• Robustness of community structure in networks
• Modeling social networks from sampled data
• Inferring the origin of an epidemic with a dynamic message-passing algorithm
• Data Based Identification and Prediction of Nonlinear and Complex Dynamical Systems
• Network structure from rich but noisy data
• A heuristic approach to author name disambiguation in bibliometrics databases for large-scale research assessments
• Robust Reconstruction of Complex Networks from Sparse Data
• Traceroute sampling makes random graphs appear to have power law degree distributions
• Model-free inference of direct network interactions from nonlinear collective dynamics
• Accuracy and Scaling Phenomena in Internet Mapping
• Friendship networks and social status
• Coauthorship and citation in scientific publishing
• Reconstructing networks with unknown and heterogeneous errors
• Structural inference for uncertain networks

Cited by in corpus (21)

• Bayesian inference of network structure from unreliable data
• Linking theory and empirics: a general framework to model opinion formation processes
• The structured backbone of temporal social ties
• Community Detection in Bipartite Networks with Stochastic Blockmodels
• Criminal Networks Analysis in Missing Data scenarios through Graph Distances
• Latent Network Models to Account for Noisy, Multiply-Reported Social Network Data
• Mapping flows on sparse networks with missing links
• Layer reconstruction and missing link prediction of multilayer network with a Maximum A Posteriori estimation
• Distances and Isomorphism between Networks: Stability and Convergence of Network Invariants
• Clustering of heterogeneous populations of networks
• Compressing network populations with modal networks reveals structural diversity
• Linear regression and its inference on noisy network-linked data
• Joint Inference of Diffusion and Structure in Partially Observed Social Networks Using Coupled Matrix Factorization
• Cutting Through the Noise to Infer Autonomous System Topology
• Network-based analysis of fluid flows: Progress and outlook
• Community Detection in Weighted Multilayer Networks with Ambient Noise
• GINA: Neural Relational Inference From Independent Snapshots
• Feature Learning and Network Structure from Noisy Node Activity Data
• A principled approach for weighted multilayer network aggregation
• Network Reconstruction and Controlling Based on Structural Regularity Analysis
• Anomaly, reciprocity, and community detection in networks