The calculation of scientific publication profiles is crucial in the systematization of scientific knowledge and support for scientific decision-making. This paper proposes a method for forming publication profiles in the field of control theory, based on the integration of text analysis and coauthorship network analysis. We describe a basic algorithm that analyzes publication texts by a thematic classifier as well as its enhanced version that considers network connections within a heuristic approach. The methods are examined using expert assessments and quantitative metrics; according to the examination results, combining textual and network data significantly improves the accuracy of publication profiles. Hypotheses about a relationship between the thematic similarity and network proximity of publications are tested, and the approach proposed is validated accordingly. In addition, directions for further research are identified.