Community detection in complex networks using Extremal Optimization

We propose a novel method to find the community structure in complex networks based on an extremal optimization of the value of modularity. The method outperforms the optimal modularity found by the existing algorithms in the literature. We present the results of the algorithm for computer simulated and real networks and compare them with other approaches. The efficiency and accuracy of the method make it feasible to be used for the accurate identification of community structure in large complex networks.Comment: 4 pages, 4 figure

Theories for influencer identification in complex networks

In social and biological systems, the structural heterogeneity of interaction networks gives rise to the emergence of a small set of influential nodes, or influencers, in a series of dynamical processes. Although much smaller than the entire network, these influencers were observed to be able to shape the collective dynamics of large populations in different contexts. As such, the successful identification of influencers should have profound implications in various real-world spreading dynamics such as viral marketing, epidemic outbreaks and cascading failure. In this chapter, we first summarize the centrality-based approach in finding single influencers in complex networks, and then discuss the more complicated problem of locating multiple influencers from a collective point of view. Progress rooted in collective influence theory, belief-propagation and computer science will be presented. Finally, we present some applications of influencer identification in diverse real-world systems, including online social platforms, scientific publication, brain networks and socioeconomic systems.Comment: 24 pages, 6 figure

Mathematical control of complex systems 2013

Mathematical control of complex systems have already become an ideal research area for control engineers, mathematicians, computer scientists, and biologists to understand, manage, analyze, and interpret functional information/dynamical behaviours from real-world complex dynamical systems, such as communication systems, process control, environmental systems, intelligent manufacturing systems, transportation systems, and structural systems. This special issue aims to bring together the latest/innovative knowledge and advances in mathematics for handling complex systems. Topics include, but are not limited to the following: control systems theory (behavioural systems, networked control systems, delay systems, distributed systems, infinite-dimensional systems, and positive systems); networked control (channel capacity constraints, control over communication networks, distributed filtering and control, information theory and control, and sensor networks); and stochastic systems (nonlinear filtering, nonparametric methods, particle filtering, partial identification, stochastic control, stochastic realization, system identification)

Subitizing with Variational Autoencoders

Numerosity, the number of objects in a set, is a basic property of a given visual scene. Many animals develop the perceptual ability to subitize: the near-instantaneous identification of the numerosity in small sets of visual items. In computer vision, it has been shown that numerosity emerges as a statistical property in neural networks during unsupervised learning from simple synthetic images. In this work, we focus on more complex natural images using unsupervised hierarchical neural networks. Specifically, we show that variational autoencoders are able to spontaneously perform subitizing after training without supervision on a large amount images from the Salient Object Subitizing dataset. While our method is unable to outperform supervised convolutional networks for subitizing, we observe that the networks learn to encode numerosity as basic visual property. Moreover, we find that the learned representations are likely invariant to object area; an observation in alignment with studies on biological neural networks in cognitive neuroscience

Express Prediction Of External Distinctive Features Of Person Using The Program Of Dermatoglyphics For Prediction

The aim of our study was to investigate the current state of computer identification applications, such as artificial neural networks. The material of our study were antroposcopic and anthropometric parameters obtained from 180 male and females aged 18–55 years living in the Ivano-Frankivsk region and belonging to Boiko, Lemko or Hutsul ethno-territorial group. Prints of comb pattern of the toes obtained by scanning with Futronic\u27s FS80 USB2.0 Fingerprint Scanner using the program ftrScanApiEx.exe. followed by the transfer of data to a personal computer. For statistical processing of the obtained data we use STATISTICA 12 from the company StatSoft. Construction of neural networks was carried out using Neural Networks. As a result of our research there was carried out the prediction of anthropometric and antroposcopic parameters (ethno-territorial and gender belonging, etc.) through the use of dermatoglyphic parameters of the hands and feet in 180 people living in the Ivano-Frankivsk region. The proposed method allowed to obtain the results with a forecasts probability 73–90 %. The use of above algorithm of actions allowed a 50 % increase of quality of identification of unknown person for using dermatoglyphic method and 67 % facilitatation of the process of identification (of quantitative and qualitative calculations, determining correlations between parameters) in comparison with previously known manner. Therefore, our proposed method can be used as an express diagnostics of common phenotypic traits of the person (ethno-territorial affiliation, gender, etc.) at admission of mass victims (natural disasters, acts of terrorism, armed conflicts, man-made disasters, etc.), it doesn\u27t not require a long time for conducting, specially trained staff and is inexpensive.Conclusions: The possibility of predicting external-recognizing features of a person such as etno-racial belonging, sex, anthropometric and antroposcopic parameters will allow widely use dermatoglyphic method at the level with other methods in conducting forensic identification of impersonal, fragmented and putrefactive modified corpses

Detection of Complex Networks Modularity by Dynamical Clustering

Based on cluster de-synchronization properties of phase oscillators, we introduce an efficient method for the detection and identification of modules in complex networks. The performance of the algorithm is tested on computer generated and real-world networks whose modular structure is already known or has been studied by means of other methods. The algorithm attains a high level of precision, especially when the modular units are very mixed and hardly detectable by the other methods, with a computational effort ${\cal O}(KN)$ on a generic graph with $N$ nodes and $K$ links.Comment: 5 pages, 2 figures. Version accepted for publication on PRE Rapid Communications: figures changed and text adde

Identification of Group Changes in Blogosphere

The paper addresses a problem of change identification in social group evolution. A new SGCI method for discovering of stable groups was proposed and compared with existing GED method. The experimental studies on a Polish blogosphere service revealed that both methods are able to identify similar evolution events even though both use different concepts. Some differences were demonstrated as wellComment: The 2012 IEEE/ACM International Conference on Advances in Social Networks Analysis and Mining, IEEE Computer Society, 2012, pp. 1233-123

CIAGAN: Conditional Identity Anonymization Generative Adversarial Networks

The unprecedented increase in the usage of computer vision technology in society goes hand in hand with an increased concern in data privacy. In many real-world scenarios like people tracking or action recognition, it is important to be able to process the data while taking careful consideration in protecting people's identity. We propose and develop CIAGAN, a model for image and video anonymization based on conditional generative adversarial networks. Our model is able to remove the identifying characteristics of faces and bodies while producing high-quality images and videos that can be used for any computer vision task, such as detection or tracking. Unlike previous methods, we have full control over the de-identification (anonymization) procedure, ensuring both anonymization as well as diversity. We compare our method to several baselines and achieve state-of-the-art results.Comment: CVPR 202