Algorithm 3 Key-players Greedy Optimization
Require: Graph of the social network (adjacency matrix)
1: Selectk nodes at random to populate set S
2: SetF = fit using appropriate key-player metric
3: for each nodeu in S and each node v not in S do
4: DELTAF = improvement in fit ifu and v were swapped
5: Select pair with largest DELTAF;
6: if DELTAF<= then
7: terminate
8: else
9: swap pair with greatest improvement in fit
10: SetF = F + DELT AF
11: end if
12: end for
key-player-analysis allows for the trade-off between the number of informed participants and minimum dissemination path length for global information spread to be determined [8].
2.5
Examples of SNA from Mobility Tracking and Other Data
This section introduces different examples of how social network analysis has been applied in a number of different studies. Social networks provide a structure for information flow. Con- sequently, much of the literature in this field is relevant and some of the most interesting tech- niques have been highlighted in which social network analysis has been adopted and exploited. Considering physical social networks are based on participant (face to face) interaction, inform- ation flow has been of particular interest for business and organisational analysis. In many cases, social networks are used and extended as an analysis tool that allows communication to be discovered and exploited. Among these contributions, Cross et al. [21] propose an approach that is based on creating a sociogram for an information flow network in an organisation. Also related to organisational needs, Mueller-Prothmann & Finke [67] use social network analysis to develop a method for expert localisation and knowledge transfer. They adapt social network analysis to fit organisational practice where it provides a tool to identify knowledgeable com- munities and to analyse the structure of information flow within and between organizations. Their analysis uses a range of graph-based metrics to assess the social network structure. Dahel & Pedersen [23] use a questionnaire to examine the role of informal contacts in a specific cluster or sub-network. The authors analyse the knowledge flow and determine whether the employees
2.5 Examples of SNA from Mobility Tracking and Other Data 27
actually acquire valuable knowledge through informal information networks.
Helms & Buijsrogge [49] seek to extend social network analysis and develop a technique called knowledge network analysis. In doing so, they add various concepts that are aimed at making social network analysis more suitable for knowledge networks. These concepts include know- ledge management roles, expertise levels, knowledge velocity and knowledge viscosity. Helms et al. [50] focus on evaluating the limitations of network analysis for knowledge sharing. The evaluation is carried out through a case study at an international product software developer event. The authors compare and contrast qualitative and quantitative studies and use this to extract limitations of network analysis in the context of their work.
Fischbach et al. [36] also study information flow between workers within an organisation but extend their analysis to a range of different tools and technologies. The study uses dynamic social network analysis and includes face-to-face interaction, email and instant message com- munication. These studies show how social networks can be exposed and exploited. It is noted that identifying scenarios within which both online and offline social networks co-exist is a challenge.
Beyond the use of social network analysis in an organisational context, social network analysis has been employed to study information flow through diverse technologies. In [63], Martinez et al study collaborative support in computer aided learning. A mixed evaluation method is used that integrates quantitative statistics, qualitative data analysis and social network analysis. They combine different types of data source to support their approach where the data sources include computer log events, face-to-face interaction, questionnaires and focus groups. Email communication is the focus of Kossinets et al. [56] who study the temporal dynamics of com- munication using emails between the staff within a University, defining a network back-bone that maps the quickest information flows in the network.
Tang et al. [91] propose a new approach for information detection and tracking on blogs using both social features and text. They focus on discovering hidden communities within a social network and develop a weighted graph representation showing closeness of relations. They combine these methods to extract the information flow and investigate both temporal and spa- tial dimensions. Lim [59] looks at patterns of information flow in the chat exchanges of two virtual learning groups using social network analysis. Quantitative statistical network analysis is applied to textual data (tutorial transcripts) containing the exchanges of information within the online collaborative learning context. Information flow is also tackled from a physical perspect- ive in [99]. Statistical calculation is used to analyse the information flow of instant messages between employees in an organization, taking into account the observation that people interested in an item are likely to belong to the same social groups.