Making visible social networks. Representation, space and diagrams in Social Network Analysis Stefano Perna*, Marianna Marra**, Pasquale Napolitano*** Abstract - Aim of this paper is to describe, from a theoretical point of view, the main spatial and representational schemata rising in the field of Social Network Analysis (SNA). One of the theoretical assumptions which drive our work consists in revealing that the very use of concepts like "mapping", "flux" or "network" implies a view of social relationship based on "spatialized" or "topologic" schemata which can be represented by specific visual techniques and languages. Starting from these assumptions, the paper will analyze these issues in two steps: first it describes the rising and the diffusion of some representational models in the field of SNA; second, it considers these representational and spatial models from the point of view of a visual representation and communication theory. Keywords – Social Network, Space, Representation, Diagram I. INTRODUCTION The concept of “network” has been repeatedly invoked during the last hundred years in different fields as physics, biology, linguistics, anthropology, sociology and cognitive sciences. Our contribution intends to expose primarily a specific analysis methodology for studying social networks and their implications: Social Network Analysis (SNA). Then we highlight how it entails precise representational and spatial patterns. One of the goals which drive our work consists in revealing that the very use of concepts like "mapping", "flux" or "network" and that of specific visual techniques and languages, implies a view of social relationship that entails "spatialized" or "topological" schemata. II. SOCIAL NETWORK ANALYSIS. A BRIEF OVERVIEW SNA is an interdisciplinary methodology developed by sociologists in collaboration with mathematics, statistics which made it an attractive tool for other disciplines like economy and engineering. The first significant contributions in this field is by researchers of the School of Manchester. Thanks to their reflections “network” is discussed, for the first time, as an analytical concept to which apply the mathematical theory of graphs. * Department of Communication Sciences, University of Salerno, Via Ponte don Melillo, 84084 Fisciano, Salerno, Italy, Email: stefanoperna@fastwebnet.it ** Department of Communication Sciences, University of Salerno, Via Ponte don Melillo, 84084 Fisciano, Salerno, Italy, Email: mmarra@unisa.it *** Department of Communication Sciences, University of Salerno, Via Ponte don Melillo, 84084 Fisciano, Salerno, Italy, Email: pnapolitano@unisa.it SNA is based on an assumption of the importance of relationships among interacting nodes. It is a way to capture the interconnections of the networks . According to Pryke (2004), SNA is “a set of tools for mapping important knowledge relationship between people or department”. According to this approach, the main goals of social research are “ to trace vertical and lateral information flows , identify sources and goals, to look for constrain above resourses” (Wellman, 1997), improve flows of knowledge, found lack of connection, understand the nature of social ties and the degree of their intensity (Kilduff – Tsai, 2003). The object of analysis is always a network of nodes and interconnections Nodes can be individuals or entire organizations. The world, in fact, according to this approach is seen as composed of network rather than groups. SNA appears the best methodology to understand the links and interconnections that keep alive the network and through which the actors share information and knowledge. Given that structured social relationships are a more powerful mean of sociological explanation than the personal attributes of the members of a group, the SNA method is a useful tool to gauge relations and the structure of the network links in question and its tangible and intangible attributes. In fact, the SNA allows first to make an analysis of the structure making it visible and understandable through the form that the analysis assumes using mathematical theory of graphs. And, consequently, to analyze the nature and intensity of communication that flows through the network, allowing the researchers to understand what are the most significant information circulating among the nodes of the network. The most interesting aspect of SNA, here, is the ability to produce as a final output a “map” of the network wich includes informal, tacit and not otherwise formalized (and therefore unknown to the same members of the network) relations. With the visualization of the positions of nodes and the direction of the links we can get a great number of precise information which can be summarized in the following concepts: • density: the measure of the connectedness in a network. It can be defined as the actual number of ties in a network, expressed as a proportion of the maximum possible number of ties; • centrality: A node can be central when it has the higher number of ties with other nodes. Local centrality considers only direct ties, while global centrality considers also indirect ties; • betweenness: measures the extent to which a particular node lies “between” the various nodes in a network. Even if a node has few ties it can be play san important intermediary role; • centralization: describes the extent to which the connectedness is organized around particular focal nodes.