Piotr Bródka

GED: the method for group evolution discovery in social networks

The continuous interest in the social network area contributes to the fast development of this field. The new possibilities of obtaining and storing data facilitate deeper analysis of the entire network, extracted social groups and single individuals as well. One of the most interesting research topic is the dynamics of social groups which means analysis of group evolution over time. Having appropriate knowledge and methods for dynamic analysis, one may attempt to predict the future of the group, and then manage it properly in order to achieve or change this predicted future according to specific needs. Such ability would be a powerful tool in the hands of human resource managers, personnel recruitment, marketing, etc. The social group evolution consists of individual events and seven types of such changes have been identified in the paper: continuing, shrinking, growing, splitting, merging, dissolving and forming. To enable the analysis of group evolution a change indicator—inclusion measure was proposed. It has been used in a new method for exploring the evolution of social groups, called Group Evolution Discovery (GED). The experimental results of its use together with the comparison to two well-known algorithms in terms of accuracy, execution time, flexibility and ease of implementation are also described in the paper.

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...

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.

Predicting group evolution in the social network

Groups --- social communities are important components of entire societies, analysed by means of the social network concept. Their immanent feature is continuous evolution over time. If we know how groups in the social network has evolved we can use this information and try to predict the next step in the given group evolution. In the paper, a new aproach for group evolution prediction is presented and examined. Experimental studies on four evolving social networks revealed that (i) the prediction based on the simple input features may be very accurate, (ii) some classifiers are more precise than the others and (iii) parameters of the group evolution extracion method significantly influence the prediction quality.

Identification of Group Changes in Blogosphere

The paper addresses a problem of change identification in social group evolution. A new SGCI method for discovering of stable groups was proposed and compared with existing GED method. The experimental studies on a Polish blogosphere service revealed that both methods are able to identify similar evolution events even though both use different concepts. Some differences were demonstrated as well.

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.

Seed Selection for Spread of Influence in Social Networks: Temporal vs. Static Approach

The problem of finding optimal set of users for influencing others in the social network has been widely studied. Because it is NP-hard, some heuristics were proposed to find sub-optimal solutions. Still, one of the commonly used assumption is the one that seeds are chosen on the static network, not the dynamic one. This static approach is in fact far from the real-world networks, where new nodes may appear and old ones dynamically disappear in course of time.The main purpose of this paper is to analyse how the results of one of the typical models for spread of influence - linear threshold - differ depending on the strategy of building the social network used later for choosing seeds. To show the impact of network creation strategy on the final number of influenced nodes - outcome of spread of influence, the results for three approaches were studied: one static and two temporal with different granularities, i.e. various number of time windows. Social networks for each time window encapsulated dynamic changes in the network structure. Calculation of various node structural measures like degree or betweenness respected these changes by means of forgetting mechanism - more recent data had greater influence on node measure values. These measures were, in turn, used for node ranking and their selection for seeding.All concepts were applied to experimental verification on five real datasets. The results revealed that temporal approach is always better than static and the higher granularity in the temporal social network while seeding, the more finally influenced nodes. Additionally, outdegree measure with exponential forgetting typically outperformed other time-dependent structural measures, if used for seed candidate ranking.