Abstract
Capturing data in the form of networks is becoming an increasingly popular approach for modeling, analyzing and visualising complex phenomena, to understand the important properties of the underlying complex processes. Access to many large-scale network datasets is restricted due to the privacy and security concerns. Also for several applications (such as functional connectivity networks), generating large scale real data is expensive. For these reasons, there is a growing need for advanced mathematical and statistical models (also called generative models) that can account for the structure of these large-scale networks, without having to materialize them in the real world. The objective is to provide a comprehensible description of the network properties and to be able to infer previously unobserved properties. Various models have been developed by researchers, which generate synthetic networks that adhere to the structural properties of real networks. However, the selection of the appropriate generative model for a given real-world network remains an important challenge. In this paper, we investigate this problem and provide a novel technique (named as TripletFit) for model selection (or network classification) and estimation of structural similarities of the complex networks. The goal of network model selection is to select a generative model that is able to generate a structurally similar synthetic network for a given real-world (target) network. We consider six outstanding generative models as the candidate models. The existing model selection methods mostly suffer from sensitivity to network perturbations, dependency on the size of the networks, and low accuracy. To overcome these limitations, we considered a broad array of network features, with the aim of representing different structural aspects of the network and employed deep learning techniques such as deep triplet network architecture and simple feed-forward network for model selection and estimation of structural similarities of the complex networks. Our proposed method, outperforms existing methods with respect to accuracy, noise-tolerance, and size independence on a number of gold standard data set used in previous studies.
Highlights
Datasets emerging from different fields such as biology, neuroscience, engineering, social science, economics, etc. are often represented as networks to understand the complex systems in these fields.To understand the formation and evolution of real-world networks various generative models have been proposed to generate synthetic networks that follow the non-trivial topological properties of realworld networks [7,13,35,37,57,59]
We have several network instances generated using six generative models and many real world networks that correspond to one feature vector representation in feature dataset
We proposed a novel method for network model selection and network similarity
Summary
To understand the formation and evolution of real-world networks various generative models have been proposed to generate synthetic networks that follow the non-trivial topological properties of realworld networks [7,13,35,37,57,59]. Prior to network generation, we have to perform the non-trivial task of choosing the appropriate generative model for a particular application ( called model selection). Since we want to choose the model that is most representative of the real network, model selection involves deep analysis of the properties of the given network (called target network), and the most appropriate model is chosen. Model selection attempts to evaluate a library of candidate generative models and predict which the most appropriate for generating complex network instances similar to the real network. There are many applications of model selection including network sampling [22,34,36,54], simulation of network dynamics [12,42,46] and summarization [2,39,50] etc
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