We present a study on co-authorship network representation based on network embedding together with additional information on topic modeling of research papers and new edge embedding operator. We use the link prediction (LP) model for constructing a recommender system for searching collaborators with similar research interests. Extracting topics for each paper, we construct keywords co-occurrence network and use its embedding for further generalizing author attributes. Standard graph feature engineering and network embedding methods were combined for constructing co-author recommender system formulated as LP problem and prediction of future graph structure. We evaluate our survey on the dataset containing temporal information on National Research University Higher School of Economics over 25 years of research articles indexed in Russian Science Citation Index and Scopus. Our model of network representation shows better performance for stated binary classification tasks on several co-authorship networks.
This chapter analyses the nature of protests in Iceland, the United Kingdom and the United States in America from 2008 to 2016. We focus on the nature of these protests, forms of collective actions, main drivers of the protests and the resulting political changes. It allows us to determine that protests played the role of the challengers of the status quo — protested against the current political state in their countries and tried to develop alternatives by revoking practices of direct democ- racy, creating public spaces for discussions and promote their ideas among the broad public.
This book constitutes the proceedings of the 6th International Conference on Analysis of Images, Social Networks and Texts, AIST 2017, held in Moscow, Russia, in July 2017.
The 29 full papers and 8 short papers were carefully reviewed and selected from 127 submissions. The papers are organized in topical sections on natural language processing; general topics of data analysis; analysis of images and video; optimization problems on graphs and network structures; analysis of dynamic behavior through event data; social network analysis.
Autonomous taxies are in high demand for smart city scenario. Such taxies have a well specified path to travel. Therefore, these vehicles only required two important parameters. One is detection parameter and other is control parameter. Further, detection parameters require turn detection and obstacle detection. The control parameters contain steering control and speed control. In this paper a novel autonomous taxi model has been proposed for smart city scenario. Deep learning has been used to model the human driver capabilities for the autonomous taxi. A hierarchical Deep Neural Network (DNN) architecture has been utilized to train various driving aspects. In first level, the proposed DNN architecture classifies the straight and turning of road. A parallel DNN is used to detect obstacle at level one. In second level, the DNN discriminates the turning i.e. left or right for steering and speed controls. Two multi layered DNNs have been used on Nvidia Tesla K 40 GPU based system with Core i-7 processor. The mean squared error (MSE) for the detection parameters viz. speed and steering angle were 0.018 and 0.0248 percent, respectively, with 15 milli seconds of realtime response delay.
In this paper we show that for a given co-authorship network we could construct a recommender system for searching collaborators with similar research interests defined via keywords and topic modelling. We suggest new link embedding method and evaluate our model on National Research University Higher School of Economics (NRU HSE) co-authorship network.
Co-authorship networks contain invisible patterns of collaboration among researchers. The process of writing joint paper can depend of different factors, such as friendship, common interests, and policy of university. We show that, having a temporal co-authorship network, it is possible to predict future publications. We solve the problem of recommending collaborators from the point of link prediction using graph embedding, obtained from co-authorship network. We run experiments on data from HSE publications graph and compare it with relevant models.
One of the major problem of recommendation services is commercial astroturfing. This work is devoted to constructing a model capable of detecting astroturfing based on network analysis. The main idea of the model is projecting a multipartite network to a unipartite and detecting communities in it representing actors with falsified opinions.
Rand (1971) proposed what has since become a well-known index for comparing two partitions obtained on the same set of units. The index takes a value on the interval between 0 and 1, where a higher value indicates more similar partitions. Sometimes, e.g. when the units are observed in two time periods, the splitting and merging of clusters should be considered differently, according to the operationalization of the stability of clusters. The Rand Index is symmetric in the sense that both the splitting and merging of clusters lower the value of the index. In such a nonsymmetric case, one of the Wallace indexes (Wallace, 1983) can be used. Further, there are several cases when one wants to compare two partitions obtained on different sets of units, where the intersection of these sets of units is a non-empty set of units. In this instance, the new units and units which leave the clusters from the first partition can be considered as a factor lowering the value of the index. Therefore, a modified Rand index is presented. Because the splitting and merging of clusters have to be considered differently in some situations, an asymmetric modified Wallace Index is also proposed. For all presented indices, the correction for chance is described, which allows different values of a selected index to be compared.
The natural language structure can be viewed as weighted semantic network. Such representation gives an option to investigate the text corpus as the model of the subject domain. In this paper we propose the mechanism of the semantic network identification and construction. We apply the methodological instrument for the social media text analysis and trace the dynamics of the discussions about 1917 year within the internet communities. Network changes illustrate the changes of the interest to different topics. The proposed mechanism can be used for the monitoring of the different social processes and phenomenal in online social networks and media.
The purpose of this project is to develop a reliable and valid field survey research instrument to assess national cultural cognitive templates of preferred leader behaviour dimensions to facilitate education, development, and training of managerial leaders operating across diverse organisations.
We consider the problem of depth reconstruction from downsampled sparse depth values. We compare our approach with semi-dense depth map interpolation and direct RGB-to-Depth reconstruction solutions on several datasets, including Matterport 3D dataset containing RGB and depth images of 90 building-scale scenes. We demonstrate that the proposed model can produce approximate depth map for over two hundreds images per second.
The goal of this study is to analyze the Social Networks Journal contribution to the sphere of social network analysis and as a result, improve the methodology that reflects the theoretical contribution of empirical articles within three dimensions: theory building, theory testing and applied method. In addition, the paper includes the examination of journal co-evolution within the field of social network analysis. In this study, we build a model of social network journals and identify the place that Social Networks occupies within this network, with its unique impact.
This paper addresses the question of whether one can generate networks with a given global structure (defined by selected blockmodels, i.e., cohesive, core-periphery, hierarchical, and transitivity), considering only different types of triads. Two methods are used to generate networks: (i) the newly proposed method of relocating links; and (ii) the Monte Carlo Multi Chain algorithm implemented in the ergm package in R. Most of the selected blockmodel types can be generated by considering all types of triads. The selection of only a subset of triads can improve the generated networks’ blockmodel structure. Yet, in the case of a hierarchical blockmodel without complete blocks on the diagonal, additional local structures are needed to achieve the desired global structure of generated networks. This shows that blockmodels can emerge based only on local processes that do not take attributes into account.
Definitions: State is a type of polity that is characterized by two main dimensions: “statehood” and “stateness.” Statehood is the recognition of the state by other states as independent nation, equal to others to participate on international arena; receiving and having the “statehood” for country mean that it is a part of the “concert of nations,” such as the member of the United Nations organization. Stateness is a state capacity to sustain its territory, nation, and citizens’ welfare; it is not enough being recognized by other states as such, but also important to support this status in time
In this paper, we study style transfer applications for the photo-realistic image processing tasks. First, we present the results on image quality improvement based with photo style transfer. Second, we describe the problems of learning style transfer under geometrical constraints for processing portrait images and multi-style transfer. Finally, we give a short glimpse on application of image-to-image translation methods for updating realistic graphics for video games.
Online social networks play major role in the spread of information on a very large scale. One of the major problems is to predict information propagation using social network interactions. The main purpose of this paper is to construct heuristic model of weighted graph based on empirical data that can outperform the existing models. We suggest a new approach of constructing the model of information based on matching specific weights to a given network.
The problem of studying information waves is underestimated by researchers of the social nature of this phenomenon. This article fills this gap by conceptualizing the notion of “information wave”. Information waves in social networks have a dual nature: on the one hand, having a mathematical distribution graph (wave profile), on the other – a discrete-network distribution map. The article shows examples of analysis of information waves and both kinds of their presentation. The analysis of information waves on specific socio-political examples is conducted.
In this paper, we consider new formulation of graph embedding algorithm, while learning node and edge representation under common constraints. We evaluate our approach on link prediction problem for co-authorship network of HSE researchers’ publications. We compare it with existing structural network embeddings and feature-engineering models.
In this paper, we propose to utilize the methods of network analysis to analyze the relationship between various elements that constitute any particular research in social sciences. Four levels that determine a design of the research can be established: ontological and epistemological assumptions that determine what is the reality under the study and how can we obtain the knowledge about it; a general methodological frame that defines the object of the study and a spectrum of research questions we are allowed to pose; and, finally, a list of methods that we might use in order to get answers. All these levels are interrelated, sometimes in very confusing way. We propose to extract a preliminary set of relations between various elements from textbooks on methodology of social and political sciences and to visualize and analyze their relations using network analytic methods.