How well do network models predict observations? On the importance of predictability in network models

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Abstract

Network models are an increasingly popular way to abstract complex psychological phenomena. While studying the structure of network models has led to many important insights, little attention has been paid to how well they predict observations. This is despite the fact that predictability is crucial for judging the practical relevance of edges: for instance in clinical practice, predictability of a symptom indicates whether an intervention on that symptom through the symptom network is promising. We close this methodological gap by introducing nodewise predictability, which quantifies how well a given node can be predicted by all other nodes it is connected to in the network. In addition, we provide fully reproducible code examples of how to compute and visualize nodewise predictability both for cross-sectional and time series data.

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Statistical mechanics of complex networks

Reka Albert, Albert-László Barabási (2001)

Complex networks describe a wide range of systems in nature and society, much quoted examples including the cell, a network of chemicals linked by chemical reactions, or the Internet, a network of routers and computers connected by physical links. While traditionally these systems were modeled as random graphs, it is increasingly recognized that the topology and evolution of real networks is governed by robust organizing principles. Here we review the recent advances in the field of complex networks, focusing on the statistical mechanics of network topology and dynamics. After reviewing the empirical data that motivated the recent interest in networks, we discuss the main models and analytical tools, covering random graphs, small-world and scale-free networks, as well as the interplay between topology and the network's robustness against failures and attacks.

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High-dimensional graphs and variable selection with the Lasso

Nicolai Meinshausen, Peter Bühlmann (2006)

The pattern of zero entries in the inverse covariance matrix of a multivariate normal distribution corresponds to conditional independence restrictions between variables. Covariance selection aims at estimating those structural zeros from data. We show that neighborhood selection with the Lasso is a computationally attractive alternative to standard covariance selection for sparse high-dimensional graphs. Neighborhood selection estimates the conditional independence restrictions separately for each node in the graph and is hence equivalent to variable selection for Gaussian linear models. We show that the proposed neighborhood selection scheme is consistent for sparse high-dimensional graphs. Consistency hinges on the choice of the penalty parameter. The oracle value for optimal prediction does not lead to a consistent neighborhood estimate. Controlling instead the probability of falsely joining some distinct connectivity components of the graph, consistent estimation for sparse graphs is achieved (with exponential rates), even when the number of variables grows as the number of observations raised to an arbitrary power.

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Mental Disorders as Causal Systems: A Network Approach to Posttraumatic Stress Disorder

R J McNally, D. Robinaugh, G. Wu … (2015)

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Author and article information

Contributors

Jonas M. B. Haslbeck: jonashaslbeck@gmail.com , https://jmbh.github.io/

Journal

Journal ID (nlm-ta): Behav Res Methods

Journal ID (iso-abbrev): Behav Res Methods

Title: Behavior Research Methods

Publisher: Springer US (New York )

ISSN (Print): 1554-351X

ISSN (Electronic): 1554-3528

Publication date (Electronic): 17 July 2017

Publication date PMC-release: 17 July 2017

Publication date (Print): 2018

Volume: 50

Issue: 2

Pages: 853-861

Affiliations

ISNI 0000000084992262, GRID grid.7177.6, Psychological Methods, , University of Amsterdam, ; Nieuwe Achtergracht 129-B, 1018 WT Amsterdam, Netherlands

Article

Publisher ID: 910

DOI: 10.3758/s13428-017-0910-x

PMC ID: 5880858

PubMed ID: 28718088

SO-VID: f554cfda-57ef-47ae-8aca-4f5d47d82db3

License:

Open AccessThis article is distributed under the terms of the Creative Commons Attribution 4.0 International License (http://creativecommons.org/licenses/by/4.0/), which permits unrestricted use, distribution, and reproduction in any medium, provided you give appropriate credit to the original author(s) and the source, provide a link to the Creative Commons license, and indicate if changes were made.