AIST: International Conference on Analysis of Images, Social Networks and Texts. 8th International Conference, AIST 2019, Kazan, Russia, July 17–19, 2019, Revised Selected Papers
This book constitutes the proceedings of the 8th International Conference on Analysis of Images, Social Networks and Texts, AIST 2019, held in Kazan, Russia, in July 2019.
The 24 full papers and 10 short papers were carefully reviewed and selected from 134 submissions (of which 21 papers were rejected without being reviewed). The papers are organized in topical sections on general topics of data analysis; natural language processing; social network analysis; analysis of images and video; optimization problems on graphs and network structures; analysis of dynamic behaviour through event data.
The common approach to predict the price of residential property is the hedonic price model and its extension to the case of spatial autoregression. The hedonic approach models the dependence between the price and internal characteristics of an apartment, house characteristics and external characteristics. To account for the unobserved quality of the surrounding environment price model includes factors of spatial price correlation, where the distance is usually measured as the distance in geographic space. Determining the price the seller focuses not only on the observed and unobserved factors of the apartment, house and its environment but also on the prices of similar marketed objects which can be selected both by geographic proximity and by characteristics similarity. In this paper, we use ensemble clustering approach to measure objects proximity and test that the proximity of objects in the characteristics space along with spatial correlation explains the significant variation in prices that in turn leads to an improvement of predictive ability of the model.
The paper discusses the problems of preventing harmful information spreading in social Networks. Social networks are widespread nowadays and are used not only for managers and marketers propagation of advertising, promotion of goods, but also by attackers to spread harmful information. Thus, there is a need to counter the attackers. This paper presents simulation tools and several features that contribute to the successful application for modeling social networks and examine different strategies preventing rumors and harmful information spreading. The authors cite an example of a simulation model for identifying intruders in a social network, software tools and the results of simulation experiments.