The analysis of social networks is concentrated especially on uncovering hidden relations and properties of network members (vertices). Most of the current approaches are focused mainly on different network types and different network coefficients. On one hand, the analysis can be relatively simple, on the other hand some complex approaches to network dynamics can be used. This paper introduces a novel aspect of network analysis based on the so-called Forgetting Curve. For network vertices and edges, we define two coefficients, which describe their role in the network depending on their long-term behavior. Using one of these parameters we reduce the network to smaller components. We provide some experimental results using DBLP dataset. Our research illustrates the usefulness of the proposed approach.

The analysis of social networks is concentrated mainly on uncovering hidden relations and properties of network nodes (vertices). Most of the current approaches are focused on different network types and different network coefficients. This article introduces a social network analysis based on the so-called Forgetting Curve and Swarm Intelligence inspired by the Ant Colony Optimization. We analyse a co-authorship network and identify two types of ties among its nodes. The Forgetting Curve and Swarm Intelligence are used to model the dynamics of such a network.

This paper introduces a novel method for semantic analysis of Web pages. Analysis is performed with regard to unwritten and empirically proven agreement between users and Web designers using Web patterns. This method is based on extraction of patterns which are characteristics for concrete domain. Patterns provide formalization of the agreement and allow assignment of semantics to parts of Web pages. Experimental results verify the effectives of the proposed method