Noise-tolerant model selection and parameter estimation for complex networks

Aliakbary, Sadegh; Motallebi, Sadegh; Rashidian, Sina; Habibi, Jafar; Movaghar, Ali

doi:10.1016/j.physa.2015.02.032

Cited by 7 publications

(14 citation statements)

References 39 publications

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“…In the literature, several network model selection (or network classification) methods are available most of them are based on graphlet counting feature [26,41,48], and combination of local and global features of network topology [2,4,43] for selecting the best generative model. Other methods are also developed for model selection problem [20,27].…”

Section: Related Workmentioning

confidence: 99%

“…To measure the structural similarities between two networks various quantitative measures have been reported [4,10,17,40,[48][49][50][51]62]. Graph isomorphism is one of the classical approaches to compare two graphs.…”

Section: Related Workmentioning

confidence: 99%

“…In this study, we have taken a gold standard data set 1 named as Reference Networks Datasets (RND) used in the previous study [4] that describes a Noise-tolerant model selection method for complex networks. For evaluating the network model selection they consider six network generative models: Kronecker Graphs (KG) [35], Forest Fire model (FF) [37], Barabási-Albert model (BA) [7], Watts-Strogatz model (WS) [59], Erdös-Rényi (ER) [19] and Random Power Law (RP) [57].…”

Section: Dataset Descriptionmentioning

confidence: 99%

“…For evaluating the network model selection they consider six network generative models: Kronecker Graphs (KG) [35], Forest Fire model (FF) [37], Barabási-Albert model (BA) [7], Watts-Strogatz model (WS) [59], Erdös-Rényi (ER) [19] and Random Power Law (RP) [57]. The detailed description of the dataset is given in the referred study [4].…”

Section: Dataset Descriptionmentioning

confidence: 99%

“…The selected models are the state of the art methods of network generation. Existing model selection methods have ignored some new and important generative models such as Kroneckr graphs and Forest Fire [4]. All the models are briefly described in the study [4].…”

Section: Network Modelsmentioning

confidence: 99%

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Deep learning based network similarity for model selection

Singh

Verma

Vig³

2021

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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.

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Section: Related Workmentioning

confidence: 99%

Section: Related Workmentioning

confidence: 99%

Section: Dataset Descriptionmentioning

confidence: 99%

Section: Dataset Descriptionmentioning

confidence: 99%

Section: Network Modelsmentioning

confidence: 99%

See 3 more Smart Citations

Deep learning based network similarity for model selection

Singh

Verma

Vig³

2021

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Automatic generation of adaptive network models based on similarity to the desired complex network

Attar

Aliakbary

Nezhad

2020

Physica A: Statistical Mechanics and its Applications

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Network classification-based structural analysis of real networks and their model-generated counterparts

Nagy

Molontay

2022

Net Sci

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Data-driven analysis of complex networks has been in the focus of research for decades. An important area of research is to study how well real networks can be described with a small selection of metrics, furthermore how well network models can capture the relations between graph metrics observed in real networks. In this paper, we apply machine-learning techniques to investigate the aforementioned problems. We study 500 real-world networks along with 2000 synthetic networks generated by four frequently used network models with previously calibrated parameters to make the generated graphs as similar to the real networks as possible. This paper unifies several branches of data-driven complex network analysis, such as the study of graph metrics and their pair-wise relationships, network similarity estimation, model calibration, and graph classification. We find that the correlation profiles of the structural measures significantly differ across network domains and the domain can be efficiently determined using a small selection of graph metrics. The structural properties of the network models with fixed parameters are robust enough to perform parameter calibration. The goodness-of-fit of the network models highly depends on the network domain. By solving classification problems, we find that the models lack the capability of generating a graph with a high clustering coefficient and relatively large diameter simultaneously. On the other hand, models are able to capture exactly the degree-distribution-related metrics.

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Noise-tolerant model selection and parameter estimation for complex networks

Cited by 7 publications

References 39 publications

Deep learning based network similarity for model selection

Deep learning based network similarity for model selection

Automatic generation of adaptive network models based on similarity to the desired complex network

Network classification-based structural analysis of real networks and their model-generated counterparts

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