Editing Social network analysis (section)

==Modelling and visualization of networks==
[[File:Social network characteristics diagram.jpg|thumb|upright=1.5|Different characteristics of social networks. A, B, and C show varying centrality and density of networks; panel D shows network closure, i.e., when two actors, tied to a common third actor, tend to also form a direct tie between them. Panel E represents two actors with different attributes (e.g., organizational affiliation, beliefs, gender, education) who tend to form ties. Panel F consists of two types of ties: friendship (solid line) and dislike (dashed line). In this case, two actors being friends both dislike a common third (or, similarly, two actors that dislike a common third tend to be friends).]]
Visual representation of social networks is important to understand the network data and convey the result of the analysis.<ref>{{cite journal|url=http://www.cmu.edu/joss/content/articles/volume1/Freeman.html|author=Linton C. Freeman|title=Visualizing Social Networks|journal=Journal of Social Structure|volume=1}}</ref> Numerous methods of visualization for data produced by social network analysis have been presented.<ref>{{cite journal|last=Hamdaqa|first=Mohammad |author2=Tahvildari, Ladan |author3=LaChapelle, Neil |author4=Campbell, Brian|title=Cultural Scene Detection Using Reverse Louvain Optimization|journal=Science of Computer Programming|date=2014|doi=10.1016/j.scico.2014.01.006|volume=95|pages=44–72|url=https://zenodo.org/record/889712 |doi-access=free}}</ref><ref>{{cite conference|author=Bacher, R.|year=1995|title=Graphical Interaction and Visualization for the Analysis and Interpretation of Contingency Analysis Result|chapter=Graphical interaction and visualization for the analysis and interpretation of contingency analysis results |conference=Proceedings of the 1995 Power Industry Computer Applications|pages=128–134|location=Salt Lake City, USA|publisher=IEEE Power Engineering Society|doi=10.1109/PICA.1995.515175|isbn=0-7803-2663-6 }}</ref><ref>{{cite journal | last1 = Caschera | first1 = M. C. | last2 = Ferri | first2 = F. | last3 = Grifoni | first3 = P. | year = 2008 | title = SIM: A dynamic multidimensional visualization method for social networks | journal = PsychNology Journal | volume = 6 | issue = 3| pages = 291–320 }}</ref><ref>{{Cite web |title=Network Analysis and Modeling (CSCI 5352) |url=https://danlarremore.com/5352/ |access-date=2024-12-02 |website=danlarremore.com}}</ref> Many of the [[Social network analysis software|analytic software]] have modules for network visualization. The data is explored by displaying nodes and ties in various layouts and attributing colors, size, and other advanced properties to nodes. Visual representations of networks may be a powerful method for conveying complex information. Still, care should be taken in interpreting node and graph properties from visual displays alone, as they may misrepresent structural properties better captured through quantitative analyses.<ref name="interpreting"/>

[[Signed graph]]s can be used to illustrate good and bad relationships between humans. A positive edge between two nodes denotes a positive relationship (friendship, alliance, dating), and a negative edge denotes a negative relationship (hatred, anger). Signed social network graphs can be used to predict the future evolution of the graph. In [[Signed network|signed social networks]], there is the concept of "balanced" and "unbalanced" cycles. A balanced cycle is defined as a [[Cycle (graph theory)|cycle]] where the product of all the signs are positive. According to [[balance theory]], balanced graphs represent a group of people who are unlikely to change their opinions of the other people in the group. Unbalanced graphs represent a group of people who are very likely to change their opinions of the people in their group. For example, a group of 3 people (A, B, and C) where A and B have a positive relationship, B and C have a positive relationship, and yet C and A have a negative relationship, is an unbalanced cycle. This group is very likely to change into a balanced cycle, such as one where B only has a good relationship with A, and both A and B have a negative relationship with C. By using the concepts of balanced and unbalanced graphs, the evolution of a [[social network graph]] may be forecasted.<ref>{{cite journal |last1=Cartwright |first1=Dorwin |last2=Harary |first2=Frank |title=Structural balance: a generalization of Heider's theory. |journal=Psychological Review |date=1956 |volume=63 |issue=5 |pages=277–293 |doi=10.1037/h0046049 |pmid=13359597 |s2cid=14779113 }}</ref>

Different approaches to participatory network mapping have proven useful, especially when using social network analysis as a tool for facilitating change. Here, participants/interviewers provide network data by mapping the network (with pen and paper or digitally) during the data collection session. An example of a pen-and-paper network mapping approach, which also includes the collection of some actor attributes (perceived influence and goals of actors) is the * [[Net-map toolbox]]. One benefit of this approach is that it allows researchers to collect qualitative data and ask clarifying questions while the network data is collected.<ref name="visualizing"/>

===Social networking potential===
Social Networking Potential (SNP) is a numeric [[coefficient]], derived through [[algorithm]]s<ref>{{cite book |doi=10.1145/2024288.2024326 |chapter=Measuring influence on Twitter |title=Proceedings of the 11th International Conference on Knowledge Management and Knowledge Technologies - i-KNOW '11 |year=2011 |last1=Anger |first1=Isabel |last2=Kittl |first2=Christian |page=1 |isbn=9781450307321 |s2cid=30427 }}</ref><ref>{{cite journal |last1=Riquelme |first1=Fabián |last2=González-Cantergiani |first2=Pablo |title=Measuring user influence on Twitter: A survey |journal=Information Processing & Management |date=September 2016 |volume=52 |issue=5 |pages=949–975 |doi=10.1016/j.ipm.2016.04.003 |arxiv=1508.07951 |s2cid=16343144 }}</ref> to represent both the size of an individual's [[social network]] and their ability to influence that network. SNP coefficients were first defined and used by Bob Gerstley in 2002. A closely related term is [[Social marketing intelligence#Alpha users|Alpha User]], defined as a person with a high SNP.

SNP coefficients have two primary functions:
# The [[categorization|classification]] of individuals based on their social networking potential, and
# The weighting of [[wikt:respondent|respondents]] in quantitative [[marketing research]] studies.

By calculating the SNP of respondents and by [[Behavioral targeting|targeting]] High SNP respondents, the [[Persuasion|strength]] and [[relevance]] of quantitative marketing research used to drive [[viral marketing]] strategies is enhanced.

[[Variable (research)|Variables]] used to calculate an individual's SNP include but are not limited to: participation in Social Networking activities, group memberships, leadership roles, recognition, publication/editing/contributing to non-electronic media, publication/editing/contributing to electronic media (websites, blogs), and frequency of past distribution of information within their network. The acronym "SNP" and some of the first algorithms developed to quantify an individual's social networking potential were described in the white paper "Advertising Research is Changing" (Gerstley, 2003) See [[Viral Marketing]].<ref>{{cite book|last1=(Hrsg.)|first1=Sara Rosengren|title=The Changing Roles of Advertising|date=2013|publisher=Springer Fachmedien Wiesbaden GmbH|location=Wiesbaden|isbn=9783658023645|url=https://www.springer.com/us/book/9783658023645|access-date=22 October 2015}}{{page needed|date=November 2021}}</ref>

The first book<ref>Ahonen, T. T., Kasper, T., & Melkko, S. (2005). 3G marketing: communities and strategic partnerships. John Wiley & Sons.</ref> to discuss the commercial use of Alpha Users among mobile telecoms audiences was 3G Marketing by Ahonen, Kasper and Melkko in 2004. The first book to discuss Alpha Users more generally in the context of [[social marketing intelligence]] was Communities Dominate Brands by Ahonen & Moore in 2005. In 2012, Nicola Greco ([[University College London|UCL]]) presents at [[TEDx]] the Social Networking Potential as a parallelism to the [[potential energy]] that users generate and companies should use, stating that "SNP is the new asset that every company should aim to have".<ref>{{cite web|url=http://tedxtalks.ted.com/video/TEDxMilano-Nicola-Greco-on-math;search%3Atag%3A"technology"|title=Watch "TEDxMilano – Nicola Greco – on math and social network" Video at TEDxTalks|work=TEDxTalks}}</ref>