Community Partitioning over Feature-Rich Networks Using an Extended K-Means Method.

This paper proposes a meaningful and effective extension of the celebrated K-means algorithm to detect communities in feature-rich networks, due to our assumption of non-summability mode. We least-squares approximate given matrices of inter-node links and feature values, leading to a straightforward extension of the conventional K-means clustering method as an alternating minimization strategy for the criterion. This works in a two-fold space, embracing both the network nodes and features.

Least-squares community extraction in feature-rich networks using similarity data.

We explore a doubly-greedy approach to the issue of community detection in feature-rich networks. According to this approach, both the network and feature data are straightforwardly recovered from the underlying unknown non-overlapping communities, supplied with a center in the feature space and intensity weight(s) over the network each. Our least-squares additive criterion allows us to search for communities one-by-one and to find each community by adding entities one by one.

[A syntaxonomic analysis of the initial stage effect on secondary autogenous succession of broad-leaved forest].

Using successional system of Stachyo sylvaticae-Tilietum cordatae association as a case study, the possibilities are considered of applying syntaxonomy as developed on the basis of floristic classification principles. Characteristics of restorative successions on cut-over lands have been analysed at strong and weak disturbance of soil cover as well as in plantings of coenotically weak species Pinus sylvestris and coenotically strong species Picea obovata. High self-restoring potential of the association studied is emphasised.

[The contribution of L.G. Ramensky theoretical legacy to modern vegetation science (to the 130 anniversary of the scientist's birth)].

L.G. Ramensky (1884-1953) was an outstanding Soviet geobotanist of the first part of XX century.