Cooperation of enterprises in innovative activities on the example of Polish regions

Research background: Cooperation is an indispensable element of innovation activities of enterprises. Undertakings in innovation, by nature, require not only significant expenditures or creative human capital, but also cooperation with other enterprises, scientific institutions, and the business environment. Cooperating companies have a greater chance to compete in the international arena while building the innovative potential of the regional environment. Stimulating the permanent cooperation of enterprises operating in the region, in its various forms, is a key challenge for central government authorities, local government authorities, and other actors in social and economic life. The existing literature on the cooperation of enterprises focuses on the motives of cooperation and their effects, but to a lesser extent on the spatial similarity of these processes, i.e. in individual regions of the country. Purpose of the article: The purpose of the article is to diagnose and evaluate regional differences in the level of cooperation between companies conducting innovation activities in Poland. The study used data on innovation activities published by Statistics Poland and data on enterprises participating in cluster cooperation published by the Polish Agency for Enterprise Development. Methods: The cluster analysis was used, allowing to identify voivodeships that are similar to each other in terms of the analyzed phenomenon. The spatial classification of voivodeships was performed using the Ward method, which is classified as hierarchical and is often used to group objects. The result is a hierarchical tree that groups the regions in increasingly larger clusters. Findings & value added: The results indicate a low level of cooperation and the existence of significant differences in the cooperation of enterprises as part of innovation activities in Polish regions. The low level of cooperation between companies in individual regions corresponds to the number of marketed innovations. There are similarities between some regions in terms of cooperation on innovation activities. The results of the analysis may provide an indication for national and European Union economic policy entities in the field of creating instruments to support the cooperation of companies on innovation activities.

A non-binary hierarchical tree overlapping community detection based on multi-dimensional similarity

Overlapping communities exist in real networks, where the communities represent hierarchical community structures, such as schools and government departments. A non-binary tree allows a vertex to belong to multiple communities to obtain a more realistic overlapping community structure. It is challenging to select appropriate leaf vertices and construct a hierarchical tree that considers a large amount of structural information. In this paper, we propose a non-binary hierarchical tree overlapping community detection based on multi-dimensional similarity. The multi-dimensional similarity fully considers the local structure characteristics between vertices to calculate the similarity between vertices. First, we construct a similarity matrix based on the first and second-order neighbor vertices and select a leaf vertex. Second, we expand the leaf vertex based on the principle of maximum community density and construct a non-binary tree. Finally, we choose the layer with the largest overlapping modularity as the result of community division. Experiments on real-world networks demonstrate that our proposed algorithm is superior to other representative algorithms in terms of the quality of overlapping community detection.

Hierarchical text classification using Relative Inverse Document Frequency

Automatic hierarchical text classification has been a challenging and in-needed task with an increasing of hierarchical taxonomy from the booming of knowledge organization. The hierarchical structure identifies the relationships of dependence between different categories in which can be overlapped of generalized and specific concepts within the tree. This paper presents the use of frequency of the occurring term in related categories among the hierarchical tree to help in document classification. The four extended term weighting of Relative Inverse Document Frequency (IDFr) including its located category, its parent category, its sibling categories and its child categories are exploited to generate a classifier model using centroid-based technique. From the experiment on hierarchical text classification of Thai documents, the IDFr achieved the best accuracy and F-measure as 53.65% and 50.80% in Top-n features set from family-based evaluation in which are higher than TF-IDF for 2.35% and 1.15% in the same settings, respectively.