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In this paper we analyze a collection of bibliographic networks, constructed from the data from the Web of Science on works (papers, books, etc.) on the topic of topological indices and on relating scientific fields. We present the general outlook and more specific findings about authors, works and journals, subtopics and keywords and also important relations between them based on scientometric approaches like the strongest and main citation paths, the main themes on citation path based on keywords, results of co-authorship analysis in form of the most prominent islands of citing authors, groups of collaborating authors, two-mode cores of authors and works. We investigate the nature of citing of authors, important journals and citing of works between them, journals preferred by authors and expose hierarchy of similar collaborating authors, based on keywords they use. We perform temporal analysis on one important journal as well. We give a comprehensive scientometric insight into the field of topological indices.

Hierarchical network clustering is an approach to find tightly and internally connected clusters (groups or communities) of nodes in a network based on its structure. Instead of nodes, it is possible to cluster links of the network. The sets of nodes belonging to clusters of links can overlap. While overlapping clusters of nodes are not always expected, they are natural in many applications. Using appropriate dissimilarity measures, we can complement the clustering strategy to consider, for example, the semantic meaning of links or nodes based on their properties. We propose a new hierarchical link clustering algorithm which in comparison to existing algorithms considers node and/or link properties (descriptions, attributes) of the input network alongside its structure using monotonic dissimilarity measures. The algorithm determines communities that form connected subnetworks (relational constraint) containing locally similar nodes with respect to their description. It is only implicitly based on the corresponding line graph of the input network, thus reducing its space and time complexities. We investigate both complexities analytically and statistically. Using provided dissimilarity measures, our algorithm can, in addition to the general overlapping community structure of input networks, uncover also related subregions inside these communities in a form of hierarchy. We demonstrate this ability on real-world and artificial network examples.

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