Social Network Analysis for the network scientist

3 minute read


Since I moved to IUNI as an Assistant Research Scientist, I have been struggling to communicate in a precise scientific language with network scientist in Sociology. The main problem being that the same terms I have learned from network scientists in Informatics do not always mean exactly the same thing in the two disciplines.
So here I am going to try and put together a list of resources for Informatics/Physics trained network scientist to better navigate the long history of Social Network Analysis in Sociology.

DISCLAIMER: Actually the SNA Wikipedia page is pretty amazing and easy to read. Here is and excerp, but if you are interested I would suggest to read it that.


Homophily: The extent to which actors form ties with similar versus dissimilar others. Similarity can be defined by gender, race, age, occupation, educational achievement, status, values or any other salient characteristic. Homophily is also referred to as assortativity.

Multiplexity: The number of content-forms contained in a tie. For example, two people who are friends and also work together would have a multiplexity of 2.Multiplexity has been associated with relationship strength.

Mutuality/Reciprocity: The extent to which two actors reciprocate each other’s friendship or other interaction.

Network Closure: A measure of the completeness of relational triads. An individual’s assumption of network closure (i.e. that their friends are also friends) is called transitivity. Transitivity is an outcome of the individual or situational trait of Need for Cognitive Closure.

Propinquity: The tendency for actors to have more ties with geographically close others.


Bridge: An individual whose weak ties fill a structural hole, providing the only link between two individuals or clusters. It also includes the shortest route when a longer one is unfeasible due to a high risk of message distortion or delivery failure.

Centrality: Centrality refers to a group of metrics that aim to quantify the “importance” or “influence” (in a variety of senses) of a particular node (or group) within a network.Examples of common methods of measuring “centrality” include betweenness centrality,closeness centrality, eigenvector centrality, alpha centrality, and degree centrality.

Density: The proportion of direct ties in a network relative to the total number possible.

Distance: The minimum number of ties required to connect two particular actors, as popularized by Stanley Milgram’s small world experiment and the idea of ‘six degrees of separation’.

Structural holes: The absence of ties between two parts of a network. Finding and exploiting a structural hole can give an entrepreneur a competitive advantage. This concept was developed by sociologist Ronald Burt, and is sometimes referred to as an alternate conception of social capital.

Tie Strength: Defined by the linear combination of time, emotional intensity, intimacy and reciprocity (i.e. mutuality).Strong ties are associated with homophily, propinquity and transitivity, while weak ties are associated with bridges.


Groups are identified as ‘cliques’ if every individual is directly tied to every other individual, ‘social circles’ if there is less stringency of direct contact, which is imprecise, or as structurally cohesive blocks if precision is wanted.

Clustering coefficient: A measure of the likelihood that two associates of a node are associates. A higher clustering coefficient indicates a greater ‘cliquishness’.

Cohesion: The degree to which actors are connected directly to each other by cohesive bonds. Structural cohesion refers to the minimum number of members who, if removed from a group, would disconnect the group.

Further Reading on centrality measures

Robert A. Hanneman and Mark Riddle Introduction to social network methods
Francis Bloch, Matthew O. Jackson, Pietro Tebaldi Centrality Measures in Networks
Phillip Bonacich Power and Centrality: A Family of Measures
Based on materials by Lada Adamic, UMichigan Network Centrality