Effects of missing data in social networks

  title={Effects of missing data in social networks},
  author={Gueorgi Kossinets},
  journal={Soc. Networks},
Comparison of Methods for Imputing Social Network Data
Evaluating a more extensive set of eight network imputation techniques under more practical conditions shows that the effectiveness of imputation methods differs by missing data types, missing data mechanisms, the evaluation criteria used, and the complexity of the social networks.
Imputation of missing network data: Some simple procedures
This paper investigates the use of some simple imputation procedures to handle missing network data, and the results of a simulation study show that ignoring the missing data can have large negative effects on structural properties of the network.
The Impact of Non-Response Treatments on the Stability of
The Adjusted Rand Index and the proportion of incorrect blocks in a blockmodel to describe the level of (dis)similarity are used and the amount and type of non-response, as well as the treatments of this form of missing data, all have an impact on the resulting blockmodel structures.
Missing Data in Multiplex Networks: A Preliminary Study
The main reasons for missingness in multiple-network systems are discussed, then the relation between various types of missing information and their effect on network properties are explored.
Stability of centrality measures in valued networks regarding different actor non-response treatments and macro-network structures
Simulations indicate that the amount of non-respondents, the type of underlying macro-structure, and the employed treatment have an impact on centrality scores, and regardless of the underlying network structure the median of the 3-nearest neighbors based on incoming ties performs the best.
Empirical methods for networks data: social effects, network formation and measurement error
In many contexts we may be interested in understanding whether direct connections between agents, such as declared friendships in a classroom or family links in a rural village, affect their
Structural effects of network sampling coverage I: Nodes missing at random
Fixed choice design and augmented fixed choice design for network data with missing observations
Novel methods for accounting for the FCD censoring are introduced and a new survey design is introduced, which is called the augmented fixed choice design (AFCD), which adds considerable information to analyses without unduly burdening the survey respondent, resulting in improvements over the F CD, and other existing estimators.


Continued research on data quality is needed; beyond improved samples and further investigation of the informant accuracy/reliability issue, this should cover common indices of network structure, address the consequences of sampling portions of a network, and examine the robustness of indicators ofnetwork structure and position to both random and nonrandom errors of measurement.
Mixing patterns in networks.
  • M. Newman
  • Computer Science
    Physical review. E, Statistical, nonlinear, and soft matter physics
  • 2003
This work proposes a number of measures of assortative mixing appropriate to the various mixing types, and applies them to a variety of real-world networks, showing that assortsative mixing is a pervasive phenomenon found in many networks.
Community structure in social and biological networks
  • M. Girvan, M. Newman
  • Computer Science
    Proceedings of the National Academy of Sciences of the United States of America
  • 2002
This article proposes a method for detecting communities, built around the idea of using centrality indices to find community boundaries, and tests it on computer-generated and real-world graphs whose community structure is already known and finds that the method detects this known structure with high sensitivity and reliability.
Social Network Analysis: Methods and Applications
This paper presents mathematical representation of social networks in the social and behavioral sciences through the lens of dyadic and triadic interaction models, which provide insights into the structure and dynamics of relationships between actors and groups.
A note on missing network data in the general social survey
Ego-centered networks and the ripple effect
Network robustness and fragility: percolation on random graphs.
This paper studies percolation on graphs with completely general degree distribution, giving exact solutions for a variety of cases, including site percolators, bond percolations, and models in which occupation probabilities depend on vertex degree.
Social Structure from Multiple Networks. I. Blockmodels of Roles and Positions
Networks of several distinct types of social tie are aggregated by a dual model that partitions a population while simultaneously identifying patterns of relations. Concepts and algorithms are
Network inference, error, and informant (in)accuracy: a Bayesian approach
  • C. Butts
  • Computer Science
    Soc. Networks
  • 2003