The purpose of the study: development of methods for analyzing the graph of interacting objects based on the detection of implicit communities in order to solve the problems of searching for the proximity of profiles and the exchange, distribution of information between objects.
Method: importing data from social networks with the subsequent construction of a weighted graph based on the selected attributes and the weight function corresponding to the original task; detection of communities on the constructed weighted graph and comparison of the obtained partitions with the results of classical algorithms.
Results: algorithms to construct graphs and to import attributes were developed and implemented, weight functions created, data structures were constructed, Louvain algorithm for weighted graphs was investigated and implemented with the according to data structures, additional hyper parameters that improve the quality of the standard graph partition by implicit user communities were added. On the example of the social network VKontakte, special algorithms for database crawling are built, the software and hardware complex is applied on real data, and the results of work are compared with the classical algorithms for allocating communities.
Results of the analysis of the existing approaches to protection and identification of defects (vulnerabilities and errors) in the source and binary code of software, provided on the different stages of development and maintenance. The most common defects (vulnerabilities and errors) and objects of destructive programming attacks are generalized, functional and ergonomic requirements to the modern analysis software are presented.
The paper presents major concepts of the new technology for large-scale data integration. The current enterprise data size is large, and it grows exponentially. The data management issue is even more challenging due to the heterogeneous character of the data. The technology developed encompasses a set of new object-based models, methods and software tools for representing and manipulating heterogeneous data.