CIRCA:The Hermeneutics of Network Analysis


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The hermeneutics of Network Analysis

Network Theory

The network theory is a field of research in computer science, network science, and graph theory. It has been historically applied in many disciplines, such as: biology, economics, statistical physics, communications, and sociology. Network theory is the study of graphs as a representation of either symmetric relations (directed graphs) or of asymmetric relations (undirected graphs)between distinct entities. Applications of network theory include logistical networks, the World Wide Web, Internet, gene regulatory networks, metabolic networks, social networks, epistemological networks, business networks, political connections, etc.

Graph theory

Around 1950s, as a response to a growing interest in quantitative methods of research in sociology and anthropology, a mathematical language called graph theory started being used by social scientists to help understand data from ethnographic studies. A graph is a structure built by interconnecting nodes and edges. Graph theory became a practical tool for analysis of empirical data. In the same decade, mathematicians began to understand graphs as the medium through which various modes of influence or behavior could propagate (in that time it was applied especially to study the propagation of diseases). A graph can be considered directed, meaning it represents a set of nodes connected by edges, where the edges have a direction associated with them; or indirect, when the edges have no orientation when connecting the nodes.

Humanities Computing and Network Theory

The humanities has developed some of the fundamental perceptions about the structure of social networks, although its network methodology that has processed the domains and scales where gathering data and the storage of large collections of information has been traditionally practice. There is an a vast field of opportunity with the propagation of information online, an eruption of brand new contexts where it is possible to find network data and its applications ( including big data and all types of digitally mediated ones). This way, new possibilities are wide open to produce questions, formulate theories, and analyses the contemporary society based in the network theory.

Social Network Analysis (SNA)

Social network analysis, often known by the acronym SNA, works in a distinct perspective within the social sciences. That is because social network analysis is based on an presumption of the importance of relationships between interacting components. The social network perspective encompasses theories, models, and applications that are expressed in terms of relational concepts or processes. That said, those relations defined by the links among elements (people) are a fundamental component of network theories.

Social network analysts looks at the complexity of human systems as an interconnected system of nodes (people and groups) and links (relationships and flows). Human networks are often not planned and appear as emergent systems. Their growth can be sporadic and self-organizing. Network connections end up being unevenly distributed, with some areas of the network having a high density of links and other areas of the network sparsely connected.

Metrics in Social Network Analysis

Based in the graph theory, those are some statistical measurements findings possible within social network analysis:

Betweenness - The extent to which a node rest between other nodes in the network. Betweenness takes into account the connectivity of the node's neighbors, giving a higher value for nodes which bridge clusters. The measure reflects the number of people who a person is connecting indirectly through their direct links.

Bridge - An edge is said to be a bridge if by removing it would cause its endpoints to lie in different components of a graph.

Centrality - This measure gives a close indication of the social power of a node based on how well they connect the network. In fact, "Betweenness", "Closeness", and "Degree" are all measures of centrality.

Closeness - The degree a node is near all other nodes in a network. It reflects the ability to access information.

Clustering coefficient - A measure of the likelihood that two associates of a node are associates themselves. A higher clustering coefficient indicates great click possibilities.

Degree - The count of the number of links to other actors in the network. In a direct graph, in a node, the number of head endpoints adjacent to a node is called the indegree of the node and the number of tail endpoints adjacent to a node is its out degree.

Density - The degree a nodes links knows the other proportion of links among an individual's nominees. Network or global-level density is the proportion of links in a network relative to the total number possible.

Flow betweenness centrality - The degree that a node contributes to sum of maximum flow between all pairs of nodes, but does not include that node.

Eigenvector centrality - It gives the importance of a node in a network . It assigns relative results to all nodes in the network based on the concept that connections to nodes having a high score contribute more to the results of the node in question.



Gephi is an open source software for graph and network analysis. It uses a 3D render engine to display large networks in real-time and possibilities of investigation. It is a flexible software that offers a multitask architecture that brings new possibilities to work with complex data sets and produce valuable visual results. Gephi provides easy and wide approaches to network data and allows for spatializing, filtering, navigating, manipulating, and clustering. It produces as a result visualization in forms of graphs that can be save as image files.


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