ETH, STP

SMADSC: Social Networks

Social networks often give structure to relations. They can be considered as abstract, mathematically, tractable and computationally instantiatable systems. Social networks have become a field of their own. It is very interdisciplinary touching mathematics (graph theory), computer science (algorithms), sociology (population group trends), psychology (individual and social behaviour), and complex network theory.

Interpersonal contact caused social networks to emerge. It can be understood as a descriptor for social trends (Cioffi-Revilla, 2013) . The basic elements are Nodes (units of observation), Edges (relationships), and Aggregations (Dyads, Triads, Clique, Clusters, etc.). More advanced elements are Descriptive Properties (e.g. centrality measures).

A network can also be seen as an abstract topology and “social glue”. Agents can move around the network, by jumping from node to node, either there is a connecting edge or in general. Alternatively, nodes can be mapped onto agents, either by allowing agents to move around a raster or along the edges.

A network trades off regularity and complexity, relative size and relative complexity as well as network complexity and network connectivity.

Social Network Analysis

Social Network Analysis (SNA) is based on a machine-readable representation of a social network, i.e. an adjacency matrix. While there is no “best measure” to describe a node or edge, there are several useful descriptive properties.

Bridging and spanning nodes can be identified. Also,cliques and clusters can be identified which gives a relative density of the network. Lastly, measures  of relative Connectedness and Centrality are often used (see this post).

Social Psychology

Instead of observing the network as a whole. It can be analysed from the node perspective. Nodes can be grouped into a “self” (ego) or “other (alter). The “self”‘s purpose  is “self-motivated” action relative to their role and their subjective network knowledge. If nodes are “other” then their function is that of an arbiter or reactive agent. In this view, edges represent social connectivity in the network. They represent evidence of physical, informational, and or some other material or non-material transfer or contact between nodes. Typically, the edges suggest some social binding between individuals and/or groups of nodes. Finally, an edge often connotes implicit temporal properties. Dyads are any two connected nodes in the network, whereas triads are any three connected nodes, whereas cliques are larger. Simmelian ties are strong, bidirectional social bindings.

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