Social networks have attracted much attention recently. Different studies have been conducted to automatically extract social networks among various kinds of entities from the web. Social network analysis finds its application in many current business areas. In this paper we demonstrate how the choice of the similarity measure affects ranking results of entities in a social network extracted from the web. We use different similarity measures in order to build different social networks. By applying formulas described below for each of the networks we derive a new network which is different from the original one by edge weights. Subsequently, in the derived networks we rank entities again. Finally we compare the results.

The majority of academic researchers present the results of their scientific activity on the Web. This trace can be used to derive useful information of their past, present activity and forecast the future intentions. Hence, social network of academic researchers can be of important value for scientific community. This information can be retrieved from various data source currently available on the Web. From each of them a separate net-work can be built. In this paper we present a method which can be used to combine multiple single-relational networks into a single network which will combine all relations, hence it will be multi-relational

Information on a given set of entities can be derived from multiple sources on the Web. Social networks built from these sources, using these entities as nodes, will have different edge weight values, although the entities will be the same. If these sources are different, one will not normally trust each of them equally. One source will be considered more or less importance than the other. Completely ignoring sources with little importance may yield unexpected results. In this paper, we propose a method for aggregating weight values for social networks built from the Web using different sources. First, multiple social networks are built from different data sources. Then the received edge weights are aggregated, with the importance of a data source taken into account.

Social networks have attracted much attention recently. Social network analysis finds its application in many current business areas. Different studies have been conducted to automatically extract social networks among various kinds of entities from the Web. Community detection is one of the most important and interesting research areas in social network analysis. Many works are dedicated to methods and algorithms for detecting communities in different kinds of social networks extracted on the Web. Below we give a brief description of a new method which can help to identify communities in networks.

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