Abstract
Focusing its attention on Social Network Analysis, this chapter describes a methodological proposal pertaining to the monitoring and analysis of the dynamics which characterize the collective experience of Networked Flow. Since the Networked Flow is a social process that evolves thanks to the relationships of a set of persons, its study needs techniques of inquiry that consider adequately the structural dynamics of the interactions between the involved actors. So this chapter is intended to show how the SNA could be applied to the typical interactional dynamics of the Networked Flow process. It presents three different ways of using SNA which can help the understanding and analysis of some of the typical dynamics described in the other chapters of this book. The first is the use of SNA to analyze a network’s communicative structure such as an online group. The first example of the application of SNA relates SNA indices to two variables which can be particularly relevant for the experience of networked flow, or rather, in what depth a group discusses/analyzes given subjects and the performance which originates from the group’s collective action. The second way in which SNA can be used is directed toward a longitudinal analysis of the interactions which characterize a given network of people. In this case, the chapters present the longitudinal use of SNA to monitor and analyze the evolution through time of the relations between participants in an online social network in the field of education. The need to go beyond quantitative data and to take account of the content of the exchanges within a network may however turn out to be crucial to fully understand its dynamics, above all if we consider semantic networks. The final way in which SNA is used therefore concerns the links between concepts as opposed to the links between people.
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Notes
- 1.
In graph theory, the graph, also known as sociogram, depicts a number of different lines (relations, exchanges, ties) which connect various points (actors) and represents the relational structure of the network in question. If we consider, for example, a social network (such as Facebook, MySpace, LinkedIn, among others), we are able to plot a graph in order to reproduce, in a two-dimensional space (or three-dimensional, thanks to current SNA softwares) the network of exchanges (sending messages or files) between the individuals belonging to the SN. If a group uses multiple web tools for interacting, by using an SNA incidence matrix we could also differentiate members on the base of the type of web tool used to communicate. If, however, we consider a basketball or football team, the sociogram is able to reproduce the passes of the ball from one player to another. This graphic representation can be obtained from a data matrix known as the adjacency matrix (Fig. 4.1).
Fig. 4.1 
Relational data in an adjacency matrix with relative sociogram
Referring to the example shown above, the sociogram derived from the adjacency matrix represents the network of exchanges which characterizes a basketball team. The arrows therefore represent the direction of the passes of the ball, while the numbers represent the number of such passes. It can be noted that player number 2 made five passes to player 5, whereas player 5 only passed the ball once to player 2. The tight network of exchanges between players 1–4 is quite evident, as well as the particularly central position of player 2, who, in fact, could be the playmaker as he/she is the only one who has any exchanges with player 5. Player 5, despite being more isolated, appears to be the game’s ‘finalizer’: he receives five passes from player 2 without returning the ball to him or passing it to any other members of the team; it is therefore highly likely that once he receives the ball he tries to shoot (which does not necessarily mean that he manages to score).
Figure 4.1 shows that the graph is made up of a number of lines which connect the points (or nodes) and that its construction is based on a matrix of relational data. The lines (relations or links) constitute the main unit of measurement in Graph Theory and it is from them that the structural indices which we shall later present, can be measured.
- 2.
As with any type of research, however, the collection of data in SNA constitutes a fundamental phase for as truthful an interpretation as possible of the phenomena under analysis. There are several techniques which are customarily used, and these range from observation, questionnaires, and relational interviews, to archival records, without forgetting experiments and diaries (Wasserman and Faust 1994; Garton et al. 1997). More recently, the expansion of SNA’s fields of investigation to include the internet and virtual interaction contexts has led to new types of tracking which can be added to those ‘classic’ techniques of data collection. These include log-tracking, an example of which will be described in this chapter, and, more simply, the observation of exchanges which take place within given virtual environments (such as email, web-forums, and also more complex settings like Social Networks and Virtual Worlds, e.g. Active World, Second Life or Google’s newly created Lively.
- 3.
In the authors’ view, a collaborative group includes three types of elements, the members, the tasks and the tools, and the model of overall functioning is determined by the integrated member-task-tool network which conditions individuals’ and groups’ actions and the ways in which they act. From this point of view, the incidence and adjacency matrices in SNA allow us to study the sum of these variables and to analyze the relational dynamics of a network considering the individuals, their affiliations (and individual characteristics) as well the technology used by the individuals, and in particular that which promotes interaction and contact between them, as a whole.
- 4.
Web-tracking is a quantitative technique for gathering information on what a user ‘does’ on the net (Maimon and Rokach 2005; Mazzoni and Gaffuri 2009a). Through web-tracking it is possible to “record (…) a certain number of parameters relating to the presence and time spent on web pages when connected to the server (…). It is therefore not a method of evaluation, but a method of data collection on visits made to a site” (Bastien et al. 1998).
- 5.
http://bscl.fit.fraunhofer.de/, 28 giugno 2005.
- 6.
A chain of messages characterized quence “message→reply→reply to the reply→…”.
- 7.
To analyze the structure of a web-forum, SLM uses the Mean Replay Depth proposed by David Wiley (2002) which, in the author’s view, allows us to rapidly obtain an indicator of the level of activity of the discussions on a web-forum.
- 8.
The network in which all the other participants have ties with one another.
- 9.
Following Scott’s suggestion (2000), for the rest of this discussion we will use the term centrality when referring to individual nodes, while the term centralization will be used to denote the centrality of the whole graph.
- 10.
Since, as will become clear in the following paragraph, there are many centrality and centralization indices, not all of them are calculated using a range of values from 0 to 1.
- 11.
The depth of a discussion is normally determined by the sequence “message→reply→reply to the reply→reply to the reply of the reply…” and so on.
- 12.
Sequences of lines which connect the nodes on a graph.
- 13.
- 14.
Social representations originate from a collective elaboration carried out by a group of individuals (of varying dimensions) which confronts a problem which is of some relevance to them. Social representations distinguish themselves as being the sum of the knowledge shared by each member of the group, and they assume the appearance of “common sense theories”. They are included in the concepts which take meaning from the world and order that world, and they are also among the images which offer a meaningful and comprehensible reproduction of the world.
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Gaggioli, A., Riva, G., Milani, L., Mazzoni, E. (2013). Analyzing the Experience of Networked Flow Through Social Network Analysis. In: Networked Flow. SpringerBriefs in Education. Springer, Dordrecht. https://doi.org/10.1007/978-94-007-5552-9_4
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