Examining the variability in network populations and its role in generative models

2020 ◽  
Vol 8 (S1) ◽  
pp. S43-S64 ◽  
Author(s):  
Viplove Arora ◽  
Dali Guo ◽  
Katherine D. Dunbar ◽  
Mario Ventresca
Keyword(s):  
Goodness Of Fit ◽  
Network Models ◽  
Generative Models ◽  
Data Driven ◽  
Network Data ◽  
Multiple Networks ◽  
Evaluation Scheme ◽  
Entropy Measures ◽  
Network Observation ◽  
Finite Set

AbstractA principled approach to understand networks is to formulate generative models and infer their parameters from given network data. Due to the scarcity of data in the form of multiple networks that have evolved from the same process, generative models are typically formulated to learn parameters from a single network observation, hence ignoring the natural variability of the “true” process. In this paper, we highlight the importance of variability in evaluating generative models and present two ways of quantifying the variability for a finite set of networks. The first evaluation scheme compares the statistical properties of networks in a dissimilarity space, while the other relies on data-driven entropy measures to compute variability in network populations. Using these measures, we evaluate the ability of four generative models to synthesize networks that capture the variability of the “true” process. Our empirical analysis suggests that generative models fitted for a single network observation fail to capture the variability in the network population. Our work highlights the need for rethinking the way we evaluate the goodness-of-fit of new and existing network models and devising models that are capable of matching the variability of network populations when available.

The Multilevel p2 Model

Methodology ◽  
2006 ◽  
Vol 2 (1) ◽  
pp. 42-47 ◽  
Author(s):  
Bonne J. H. Zijlstra ◽  
Marijtje A. J. van Duijn ◽  
Tom A. B. Snijders
Keyword(s):  
Social Network ◽  
Network Data ◽  
Mcmc Algorithm ◽  
Independent Observation ◽  
Single Observation ◽  
Multiple Networks ◽  
Social Network Data ◽  
Multiple Observations ◽  
Practical Support ◽  
The Social

The p 2 model is a random effects model with covariates for the analysis of binary directed social network data coming from a single observation of a social network. Here, a multilevel variant of the p 2 model is proposed for the case of multiple observations of social networks, for example, in a sample of schools. The multilevel p 2 model defines an identical p 2 model for each independent observation of the social network, where parameters are allowed to vary across the multiple networks. The multilevel p 2 model is estimated with a Bayesian Markov Chain Monte Carlo (MCMC) algorithm that was implemented in free software for the statistical analysis of complete social network data, called StOCNET. The new model is illustrated with a study on the received practical support by Dutch high school pupils of different ethnic backgrounds.

Networks

2018 ◽  
Author(s):  
Mark Newman
Keyword(s):  
Biological Networks ◽  
Generative Models ◽  
The Internet ◽  
Network Data ◽  
Computer Algorithms ◽  
Graph Models ◽  
Random Graph Models ◽  
The Social ◽  
Spectral Algorithms ◽  
Wide Availability

The study of networks, including computer networks, social networks, and biological networks, has attracted enormous interest in recent years. The rise of the Internet and the wide availability of inexpensive computers have made it possible to gather and analyse network data on an unprecendented scale, and the development of new theoretical tools has allowed us to extract knowledge from networks of many different kinds. The study of networks is broadly interdisciplinary and developments have occurred in many fields, including mathematics, physics, computer and information sciences, biology, and the social science. This book brings together the most important breakthroughts in each of these fields and presents them in a unified fashion, highlighting the strong interconnections between work in different areas. Topics covered include the measurement of networks; methods for analysing network data, including methods developed in physics, statistics, and sociology; fundamentals of graph theory; computer algorithms, including spectral algorithms and community detection; mathematical models of networks such as random graph models and generative models; and models of processes taking place on networks.

scholarly journals Data-driven control of complex networks

2021 ◽  
Vol 12 (1) ◽  
Author(s):  
Giacomo Baggio ◽  
Danielle S. Bassett ◽  
Fabio Pasqualetti
Keyword(s):  
Complex Networks ◽  
Network Dynamics ◽  
Noisy Data ◽  
Data Driven ◽  
Control Algorithms ◽  
Optimal Controls ◽  
Network Systems ◽  
The Past ◽  
Efficient Control ◽  
Finite Set

AbstractOur ability to manipulate the behavior of complex networks depends on the design of efficient control algorithms and, critically, on the availability of an accurate and tractable model of the network dynamics. While the design of control algorithms for network systems has seen notable advances in the past few years, knowledge of the network dynamics is a ubiquitous assumption that is difficult to satisfy in practice. In this paper we overcome this limitation, and develop a data-driven framework to control a complex network optimally and without any knowledge of the network dynamics. Our optimal controls are constructed using a finite set of data, where the unknown network is stimulated with arbitrary and possibly random inputs. Although our controls are provably correct for networks with linear dynamics, we also characterize their performance against noisy data and in the presence of nonlinear dynamics, as they arise in power grid and brain networks.

Using Monitoring Data to Improve HPC Performance via Network-Data-Driven Allocation

2021 ◽  
Author(s):  
Yijia Zhang ◽  
Burak Aksar ◽  
Omar Aaziz ◽  
Benjamin Schwaller ◽  
Jim Brandt ◽  
...  
Keyword(s):  
Data Driven ◽  
Monitoring Data ◽  
Network Data

scholarly journals Multi-model inference of network properties from incomplete data

2006 ◽  
Vol 3 (2) ◽  
pp. 123-136 ◽  
Author(s):  
Michael P. H. Stumpf ◽  
Thomas Thorne
Keyword(s):  
Network Models ◽  
Network Data ◽  
Global Networks ◽  
New Approach ◽  
Model Inference ◽  
Novel Approach ◽  
Eukaryotic Species ◽  
Network Properties ◽  
Experimental Approaches ◽  
Saccaromyces Cerevisiae

Summary It has previously been shown that subnets differ from global networks from which they are sampled for all but a very limited number of theoretical network models. These differences are of qualitative as well as quantitative nature, and the properties of subnets may be very different from the corresponding properties in the true, unobserved network. Here we propose a novel approach which allows us to infer aspects of the true network from incomplete network data in a multi-model inference framework. We develop the basic theoretical framework, including procedures for assessing confidence intervals of our estimates and evaluate the performance of this approach in simulation studies and against subnets drawn from the presently available PIN network data in Saccaromyces cerevisiae. We then illustrate the potential power of this new approach by estimating the number of interactions that will be detectable with present experimental approaches in sfour eukaryotic species, inlcuding humans. Encouragingly, where independent datasets are available we obtain consistent estimates from different partial protein interaction networks. We conclude with a discussion of the scope of this approaches and areas for further research

scholarly journals Individual differences among deep neural network models

2020 ◽  
Author(s):  
Johannes Mehrer ◽  
Courtney J. Spoerer ◽  
Nikolaus Kriegeskorte ◽  
Tim C. Kietzmann
Keyword(s):  
Individual Differences ◽  
Visual Recognition ◽  
Initial Conditions ◽  
Network Models ◽  
Classification Performance ◽  
Neural Network Models ◽  
Multiple Networks ◽  
Modelling Framework ◽  
Neural Computations ◽  
Category Exemplars

AbstractDeep neural networks (DNNs) excel at visual recognition tasks and are increasingly used as a modelling framework for neural computations in the primate brain. However, each DNN instance, just like each individual brain, has a unique connectivity and representational profile. Here, we investigate individual differences among DNN instances that arise from varying only the random initialization of the network weights. Using representational similarity analysis, we demonstrate that this minimal change in initial conditions prior to training leads to substantial differences in intermediate and higher-level network representations, despite achieving indistinguishable network-level classification performance. We locate the origins of the effects in an under-constrained alignment of category exemplars, rather than a misalignment of category centroids. Furthermore, while network regularization can increase the consistency of learned representations, considerable differences remain. These results suggest that computational neuroscientists working with DNNs should base their inferences on multiple networks instances instead of single off-the-shelf networks.

scholarly journals Data-driven reduction of dendritic morphologies with preserved dendro-somatic responses

2020 ◽  
Author(s):  
Willem A.M. Wybo ◽  
Jakob Jordan ◽  
Benjamin Ellenberger ◽  
Ulisses M. Mengual ◽  
Thomas Nevian ◽  
...  
Keyword(s):  
Action Potentials ◽  
Network Models ◽  
Data Driven ◽  
Fast Method ◽  
Average Difference ◽  
Neuron Models ◽  
Shape Information ◽  
Proximal Dendrite ◽  
Conductance Fluctuations ◽  
Software Toolbox

AbstractDendrites shape information flow in neurons. Yet, there is little consensus on the level of spatial complexity at which they operate. We present a flexible and fast method to obtain simplified neuron models at any level of complexity. Through carefully chosen parameter fits, solvable in the least squares sense, we obtain optimal reduced compartmental models. We show that (back-propagating) action potentials, calcium-spikes and NMDA-spikes can all be reproduced with few compartments. We also investigate whether afferent spatial connectivity motifs admit simplification by ablating targeted branches and grouping the affected synapses onto the next proximal dendrite. We find that voltage in the remaining branches is reproduced if temporal conductance fluctuations stay below a limit that depends on the average difference in input impedance between the ablated branches and the next proximal dendrite. Further, our methodology fits reduced models directly from experimental data, without requiring morphological reconstructions. We provide a software toolbox that automatizes the simplification, eliminating a common hurdle towards including dendritic computations in network models.

scholarly journals Overchoosing: A Mechanism of Tie-Formation in Social Networks

2020 ◽  
Author(s):  
Tasuku Igarashi ◽  
Johank Koskinen
Keyword(s):  
Social Networks ◽  
Formation Process ◽  
Goodness Of Fit ◽  
Network Data ◽  
Formation Processes ◽  
Friendship Network ◽  
Fundamental Mechanism

We introduce the concept of overchoosing as a fundamental mechanism of tie formation in directed social networks. The parameter represents a tendency for actors to send a lot of ties but receive few nominations back, something which implies the importance of modeling reciprocity violation as a basic tie-formation process. Analyzing large friendship network data (N = 1,575) by the stochastic actor-oriented models revealed that, under controlling for several endogenous tie formation processes, the parameter captured the formation of open triads and substantially improved the goodness of fit of the model to the data.

scholarly journals NetPyNE, a tool for data-driven multiscale modeling of brain circuits

eLife ◽  
2019 ◽  
Vol 8 ◽  
Author(s):  
Salvador Dura-Bernal ◽  
Benjamin A Suter ◽  
Padraig Gleeson ◽  
Matteo Cantarelli ◽  
Adrian Quintana ◽  
...  
Keyword(s):  
Multiple Scales ◽  
Network Models ◽  
Brain Regions ◽  
Data Driven ◽  
Model Parameters ◽  
Biophysical Modeling ◽  
Neuron Network ◽  
Graphical Interfaces ◽  
Information Theoretic Measures ◽  
High Level

Biophysical modeling of neuronal networks helps to integrate and interpret rapidly growing and disparate experimental datasets at multiple scales. The NetPyNE tool (www.netpyne.org) provides both programmatic and graphical interfaces to develop data-driven multiscale network models in NEURON. NetPyNE clearly separates model parameters from implementation code. Users provide specifications at a high level via a standardized declarative language, for example connectivity rules, to create millions of cell-to-cell connections. NetPyNE then enables users to generate the NEURON network, run efficiently parallelized simulations, optimize and explore network parameters through automated batch runs, and use built-in functions for visualization and analysis – connectivity matrices, voltage traces, spike raster plots, local field potentials, and information theoretic measures. NetPyNE also facilitates model sharing by exporting and importing standardized formats (NeuroML and SONATA). NetPyNE is already being used to teach computational neuroscience students and by modelers to investigate brain regions and phenomena.

scholarly journals Subtyping psychological distress in the population: a semi-parametric network approach

2019 ◽  
Vol 29 ◽  
Author(s):  
S. de Vos ◽  
S. Patten ◽  
E. C. Wit ◽  
E. H. Bos ◽  
K. J. Wardenaar ◽  
...  
Keyword(s):  
Psychological Distress ◽  
Community Sample ◽  
A Priori ◽  
Network Models ◽  
Optimal Choice ◽  
Data Driven ◽  
Network Density ◽  
Parametric Network ◽  
Distress Scale ◽  
Non Parametric

Abstract Aims The mechanisms underlying both depressive and anxiety disorders remain poorly understood. One of the reasons for this is the lack of a valid, evidence-based system to classify persons into specific subtypes based on their depressive and/or anxiety symptomatology. In order to do this without a priori assumptions, non-parametric statistical methods seem the optimal choice. Moreover, to define subtypes according to their symptom profiles and inter-relations between symptoms, network models may be very useful. This study aimed to evaluate the potential usefulness of this approach. Methods A large community sample from the Canadian general population (N = 254 443) was divided into data-driven clusters using non-parametric k-means clustering. Participants were clustered according to their (co)variation around the grand mean on each item of the Kessler Psychological Distress Scale (K10). Next, to evaluate cluster differences, semi-parametric network models were fitted in each cluster and node centrality indices and network density measures were compared. Results A five-cluster model was obtained from the cluster analyses. Network density varied across clusters, and was highest for the cluster of people with the lowest K10 severity ratings. In three cluster networks, depressive symptoms (e.g. feeling depressed, restless, hopeless) had the highest centrality. In the remaining two clusters, symptom networks were characterised by a higher prominence of somatic symptoms (e.g. restlessness, nervousness). Conclusion Finding data-driven subtypes based on psychological distress using non-parametric methods can be a fruitful approach, yielding clusters of persons that differ in illness severity as well as in the structure and strengths of inter-symptom relationships.

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