Preface to the special issue on “Clustering and search techniques in large scale networks”
Clustering and search techniques are essential to a wide spectrum of applications. Network clustering techniques are becoming common in the analysis of massive data sets arising in various branches of science, engineering, government and industry. In particular, network clustering and search techniques emerge as an important tool in large-scale networks.
This special issue of Optimization Letters contains refereed selected papers presented at the workshop on clustering and search techniques in large scale networks that took place on November 3–8, 2014 at the Laboratory of Algorithms and Technologies for Networks Analysis (LATNA), Higher School of Economics, in Nizhny Novgorod, Russia. The workshop was supported by the Russian Science Foundation Grant RSF 14-41-00039.
This workshop provided a forum for leading as well as beginning researchers and students to discuss recent advances and identify current and future challenges arising in research concerning clustering and search problems in large networks. The papers of this special issue reflect some the problems discussed at the workshop.
We would like to thank the valuable work of authors and reviewers for making this issue possible.
The present paper is devoted to the study of the mechanics of agent-informational clustering in a social network on the example of user segmentation tasks taking into account an influence criterion. The main features of data generated by social networks (social big data) and metrics that characterize influential network nodes are considered. A review of community-building algorithms based on the theory of social networks, as well as clustering methods based on machine learning, is carried out. Metrics for assessing the quality of segmentation are presented. The results of the application of methods (selected on the basis of the performed analysis) to a test dataset are shown. The limitations of the applicability of considered approaches and possible problems during the implementation of algorithms in the field of social network analysis are described. Evaluation of the effectiveness is performed.
This paper addresses the question of existence of relationships between usage of contemporary marketing practices and profitability for companies operating on the Russian market. To address this issue, we utilize an artificial intelligence method that so far was barely present in marketing and management science. The paper is not only promoting a novel research method, it also establishes the relationships between profitability and specific sets of marketing practices. We show that the companies having negative profitability make use of a wide spectrum of marketing practices (with an exception of interactive marketing) and they do not prioritize any specific types of practices. In contrary, profitable companies intensively use interactive marketing and also combine it with IT-marketing and network marketing. This shows that successful companies focus on relationship marketing in a variety of its forms.
Provides an overview of the developments and advances in the field of network clustering and blockmodeling over the last 10 years
This book offers an integrated treatment of network clustering and blockmodeling, covering all of the newest approaches and methods that have been developed over the last decade. Presented in a comprehensive manner, it offers the foundations for understanding network structures and processes, and features a wide variety of new techniques addressing issues that occur during the partitioning of networks across multiple disciplines such as community detection, blockmodeling of valued networks, role assignment, and stochastic blockmodeling.
Written by a team of international experts in the field, Advances in Network Clustering and Blockmodeling offers a plethora of diverse perspectives covering topics such as: bibliometric analyses of the network clustering literature; clustering approaches to networks; label propagation for clustering; and treating missing network data before partitioning. It also examines the partitioning of signed networks, multimode networks, and linked networks. A chapter on structured networks and coarsegrained descriptions is presented, along with another on scientific coauthorship networks. The book finishes with a section covering conclusions and directions for future work. In addition, the editors provide numerous tables, figures, case studies, examples, datasets, and more.Offers a clear and insightful look at the state of the art in network clustering and blockmodeling Provides an excellent mix of mathematical rigor and practical application in a comprehensive manner Presents a suite of new methods, procedures, algorithms for partitioning networks, as well as new techniques for visualizing matrix arrays Features numerous examples throughout, enabling readers to gain a better understanding of research methods and to conduct their own research effectively Written by leading contributors in the field of spatial networks analysis
Advances in Network Clustering and Blockmodeling is an ideal book for graduate and undergraduate students taking courses on network analysis or working with networks using real data. It will also benefit researchers and practitioners interested in network analysis.
This paper explores age-specific migration flows between regions of Russia. Using age-disaggregated data of the Russian Census 2010, we cluster interregional migration flows based on prevailing age-groups of migrants, analyse diversity and similarity in the choice of age-specific migration destinations and describe general socio-economic characteristics of these flows. It is for the first time that the relationship between migration and migrants’ age and life-cycle events is analysed in the Russian context. Similar to migrants in other countries, migrants in Russia choose the place of residence depending on their age. Migration flows which differ by dominating age group of migrants quite often have opposite destinations, because motivations of migration also differ. Migration follows various stages of the life-cycle: people are born in one region, study in another region, go to work in a different region, and resettle to another place after retirement. Migration modeling turns to be complicated if the impact of age factor is ignored. Therefore, the age of migrants should be considered when analyzing, modeling and interpreting interregional migration in Russia.
This two-volume set LNCS 10305 and LNCS 10306 constitutes the refereed proceedings of the 15th International Work-Conference on Artificial Neural Networks, IWANN 2019, held at Gran Canaria, Spain, in June 2019. The 150 revised full papers presented in this two-volume set were carefully reviewed and selected from 210 submissions. The papers are organized in topical sections on machine learning in weather observation and forecasting; computational intelligence methods for time series; human activity recognition; new and future tendencies in brain-computer interface systems; random-weights neural networks; pattern recognition; deep learning and natural language processing; software testing and intelligent systems; data-driven intelligent transportation systems; deep learning models in healthcare and biomedicine; deep learning beyond convolution; artificial neural network for biomedical image processing; machine learning in vision and robotics; system identification, process control, and manufacturing; image and signal processing; soft computing; mathematics for neural networks; internet modeling, communication and networking; expert systems; evolutionary and genetic algorithms; advances in computational intelligence; computational biology and bioinformatics.
This paper is focused on the automatic extraction of persons and their attributes (gender, year of born) from album of photos and videos. A two-stage approach is proposed in which, firstly, the convolutional neural network simultaneously predicts age/gender from all photos and additionally extracts facial representations suitable for face identification. Here the MobileNet is modified and is preliminarily trained to perform face recognition in order to additionally recognize age and gender. The age is estimated as the expected value of top predictions in the neural network. In the second stage of the proposed approach, extracted faces are grouped using hierarchical agglomerative clustering techniques. The birth year and gender of a person in each cluster are estimated using aggregation of predictions for individual photos. The proposed approach is implemented in an Android mobile application. It is experimentally demonstrated that the quality of facial clustering for the developed network is competitive with the state-of-the-art results achieved by deep neural networks, though implementation of the proposed approach is much computationally cheaper. Moreover, this approach is characterized by more accurate age/gender recognition when compared to the publicly available models.