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 focus of this paper is that the feature-space data part is converted into a similarity matrix format. The similarity/link values can be used in either of two modes: (a) as measured in the same scale so that one may can meaningfully compare and sum similarity values across the entire similarity matrix (summability mode), and (b) similarity values in one column should not be compared with the values in other columns (nonsummability mode). The two input matrices and two modes lead us to developing four different Iterative Community Extraction from Similarity data (ICESi) algorithms, which determine the number of communities automatically. Our experiments at real-world and synthetic datasets show that these algorithms are valid and competitive.

Stacked Community Prediction: A Distributed Stacking-Based Community Extraction Methodology for Large Scale Social Networks

Nowadays, due to the extensive use of information networks in a broad range of fields, e.g., bio-informatics, sociology, digital marketing, computer science, etc., graph theory applications have attracted significant scientific interest. Due to its apparent abstraction, community detection has become one of the most thoroughly studied graph partitioning problems. However, the existing algorithms principally propose iterative solutions of high polynomial order that repetitively require exhaustive analysis. These methods can undoubtedly be considered resource-wise overdemanding, unscalable, and inapplicable in big data graphs, such as today’s social networks. In this article, a novel, near-linear, and highly scalable community prediction methodology is introduced. Specifically, using a distributed, stacking-based model, which is built on plain network topology characteristics of bootstrap sampled subgraphs, the underlined community hierarchy of any given social network is efficiently extracted in spite of its size and density. The effectiveness of the proposed methodology has diligently been examined on numerous real-life social networks and proven superior to various similar approaches in terms of performance, stability, and accuracy.

COMPONENTS OF THE FLINT MADE INDUSTRY OF THE CUCUTENI-TRYPILLIAN COMMUNITY: EXTRACTION AND DISTRIBUTION OF FLINT

Having quite a massive and diverse source of raw materials tribes of Cucuteni-Trypillian community begin its active exploitation and utilization already from the early stages of their existence and continue throughout all their following history. Flint industry of Cucuteni-Trypillian community centers around two main scenarios: extraction — processing — distribution and / or extraction — distribution — processing of flint materials. There were a few completely different ways of flint extraction. First one (opened) was the simplest, did not require special skills and hard labor, being a simple collection of stones directly on the surface of the ground or in basseting areas (such as screes). Second one (closed) was the most complicated, requiring special tools, skills, hard labor and correspondingly more complicated organization of the community. This way implies flint extraction deep in its deposits that are often invisible from the surface crust. But it proved its value: flint miners obtained excellent high-quality materials for further processing, providing mineral wealth not only for their own communities, but also for close and remote related as well as non-Trypillian communities. We distinguished «close» and «remote» radiuses of obtained flint materials distribution. First term describes self-sustainment of separate communities with raw materials and products of its’ processing that were necessary for functioning of these collectives. Second term refers to directed massive production (extraction, processing) not only for internal needs, but mainly for exportation of obtained raw materials or finished wares, made of these materials. Also a question was raised regarding massive supply of regions that had no qualitative mineral wealth (Bug and Dnipro regions, area in — between Bug and Dnipro rivers) with flint from the proximate microregion in the Velyka Vys’ basin in contradiction to widespread belief about a more remote «donator» — Volhyn’ territory. Presence in Cucuteni-Trypillian community of flint-mining shafts, functioning of which required special skills and hard labor, specialized flint-processing workshops, transportation of raw materials and products of cleavage to remote territories became a basis for defining in this community a collective occupation, associated with flint mining and processing. Consequently, questions connected with this occupation, taking into account its’ versatility, complexity and scale should be among the basic ones in the complex study of trypillian economics.