Katarzyna Musial

Social networks on the Internet

The rapid development and expansion of the Internet and the social–based services comprised by the common Web 2.0 idea provokes the creation of the new area of research interests, i.e. social networks on the Internet called also virtual or online communities. Social networks can be either maintained and presented by social networking sites like MySpace, LinkedIn or indirectly extracted from the data about user interaction, activities or achievements such as emails, chats, blogs, homepages connected by hyperlinks, commented photos in multimedia sharing system, etc. A social network is the set of human beings or rather their digital representations that refer to the registered users who are linked by relationships extracted from the data about their activities, common communication or direct links gathered in the internet–based systems. Both digital representations named in the paper internet identities as well as their relationships can be characterized in many different ways. Such diversity yields for building a comprehensive and coherent view onto the concept of internet–based social networks. This survey provides in–depth analysis and classification of social networks existing on the Internet together with studies on selected examples of different virtual communities.

User position measures in social networks

Network analysis offers many centrality measures that are successfully utilized in the process of investigating the social network profile. The most important and representative measures are presented in the paper. It includes indegree centrality, proximity prestige, rank prestige, node position, outdegree centrality, eccentrality, closeness centrality, and betweenes centrality. Both feature analysis and experimental comparative studies revealed the general profile of selected measures.

Analysis of Neighbourhoods in Multi-layered Dynamic Social Networks

Abstract Social networks existing among employees, customers or other types of users of various IT systems have become one of the research areas of growing importance. Data about people and their interactions that exist in social media, provides information about many different types of relationships within one network. Analysing this data one can obtain knowledge not only about the structure and characteristics of the network but it also enables to understand the semantic of human relations. Each social network consists of nodes – social entities and edges linking pairs of nodes. In regular, one-layered networks, two nodes – i.e. people are connected with a single edge whereas in the multi-layered social networks, there may be many links of different types for a pair of nodes. Most of the methods used for social network analysis (SNA) may be applied only to one-layered networks. Thus, some new structural measures for multi-layered social networks are proposed in the paper. This study focuses on definitio...

Link Prediction Based on Subgraph Evolution in Dynamic Social Networks

We propose a new method for characterizing the dynamics of complex networks with its application to the link prediction problem. Our approach is based on the discovery of network sub graphs (in this study: triads of nodes) and measuring their transitions during network evolution. We define the Triad Transition Matrix (TTM) containing the probabilities of transitions between triads found in the network, then we show how it can help to discover and quantify the dynamic patterns of network evolution. We also propose the application of TTM to link prediction with an algorithm (called TTM-predictor) which shows good performance, especially for sparse networks analyzed in short time scales. The future applications and research directions of our approach are also proposed and discussed.

Local Topology of Social Network Based on Motif Analysis

Network motifs --- small subgraphs that reflect local topology can be used to discover general profile and properties of the network. Analysis of motifs for the large social networks derived from email communication is presented in the paper. The distribution of motifs in all analyzed real social networks is very similar one another and can be treated as the network fingerprint. This property is most distinctive for stronger human relationships.

Multidimensional social network: model and analysis

A social network is an abstract concept consisting of set of people and relationships linking pairs of humans. A new multidimensional model, which covers three main dimensions: relation layer, time window and group, is proposed in the paper. These dimensions have a common set of nodes, typically, corresponding to human beings. Relation layers, in turn, reflect various relationship types extracted from different user activities gathered in computer systems. The time dimension corresponds to temporal variability of the social network. Social groups are extracted by means of clustering methods and group people who are close to each other. An atomic component of the multidimensional social network is a view - small social sub-network, which is in the intersection of all dimensions. A view describes the state of one social group, linked by one type of relationship (one layer), and derived from one time period. The multidimensional model of a social network is similar to a general concept of data warehouse, in which a fact corresponds to a view. Aggregation possibilities and usage of the model is also discussed in the paper.

Multi-Layered Social Network Creation Based on Bibliographic Data

A method for extraction of the multi-layered social network based on the data about human collaborative achievements, in particular scientific papers, is presented in the paper. The objects linking people form a hierarchy, which is flattened in the pre-processing stage. Only one level of the hierarchy remains together with new activities moved from its other levels. Separate layers of the multi-layered social network are created based on these pre-processed activities.

Social capital in online social networks

The problem of social capital in context of the online social networks is presented in the paper. Not only the specific elements, which characterize the single person and influence the individuals social capital like static social capital, activity component, and social position, but also the ways of stimulation of the social capital are described.

A Performance of Centrality Calculation in Social Networks

To analyze large social networks a lot of effort and resources are usually required. Network analysis offers many centrality measures that are successfully utilized in the process of investigating the social network characteristics. One of them is node position, which can be used to assess the importance of a given node within either the whole social network or the smaller subgroup. Three algorithms that can be utilized in the process of node position evaluation are presented in the paper: PIN Edges, PIN Nodes, and PIN hybrid. Also, different algorithms for indegree and outdegree prestige measures have been developed and tested. According to the experiments performed, the algorithms based on processing of edges are always faster than the others.

A degree centrality in multi-layered social network

Multi-layered social networks reflect complex relationships existing in modern interconnected IT systems. In such a network each pair of nodes may be linked by many edges that correspond to different communication or collaboration user activities. Multi-layered degree centrality for multi-layered social networks is presented in the paper. Experimental studies were carried out on data collected from the real Web 2.0 site. The multi-layered social network extracted from this data consists of ten distinct layers and the network analysis was performed for different degree centralities measures.