Group Identification in Networks

SNA Module 3: A Conceptual Overview

A Quick Refresher

Data Management

• Collection

• Quality

• Edge List Creation

Network Terms

• Centrality

• Density

• Reciprocity

Speaker Notes:

Overview

Unlike most social science, network analysts identify groups through their relational data, not an exogenous attribute such as grade level, departmental affiliation, or years of experience. (Carolan 2014)

There are two main approaches to group identification in networks:

1. Top Down (e.g., components & communities)

2. Bottom Up (e.g, cliques, cores, & clans)

Speaker Notes:

Top Down Approaches

Components, Bi-Components, & Factions

Speaker Notes:

The “top-down” approach focuses on how larger network structures are built from smaller ones. Or stated more clearly:

…this approach takes the complete network and considers parts of it that are dense and somewhat distinct from the rest of the network.

Some of these approaches are very intuitive, others use more sophisticated “community detection” algorithms to identify groups.

Components

A component is a connected subgraph in which there is a path between all pairs of nodes.

How many “components” do you see in the network to the right?

Speaker Notes:

Bi-Components

Bi-components help to identify the important “weak” spots and ask what would happen to the network if an actor were removed.

Nodes that result in new components are referred to as “cutpoints” and are often important actors.

Speaker Notes:

Factions

A faction is a group with more ties within than across group even those group boundaries are somewhat porous.

Factions are identified by various community detection algorithms to represent these structural sub-communities.

#commoncore factions

Speaker Notes:

Discussion

Consider the following questions about a network you may be interested in studying:

• What is the boundary of this network?

• What relations within this network might be of interest to your research?

• What attributes about actors in this network might you want to capture?

• How might you collect data about these actors and their relations?

Bottom Up Approaches

Cores, Cliques, & Clans

Speaker Notes:

Like top-down methods, this approach is also concerned with how larger structures are composed of smaller groups of actors.

However, bottom-up approaches identify groups that are built up from simple dyads and triads that extend into dense clusters that give the network its “clumpiness.”

K-Cores

A K-core is a substructure (a subgraph) of the network in which each node within the K-core is connected to at least K other nodes. Therefore, a 2K-core would be a subset of actors in which a node is connected to two others.

Speaker Notes

Cliques

A n-clique is a maximally connected subgraph of nodes (> 2) in which all nodes are connected to each other.

Are there any cliques in the graph to the right? K-cores? Isolates?

Speaker Notes:

• Social network researchers can rely on several common reliability measures, including interobserver reliability, internal consistency reliability, and test–retest reliability. The latter, however, is the most commonly used. For example, we may ask a teacher nominates all those other teachers from whom he or she seeks advice about professional matters. The retest repeats the same (or similar) request at a later time. Highly correlated responses indicate high reliability.

• One thing to note when designing a network study or data collection instrument is that closer ties tend to be reported more reliably than weaker ones.

• Also, for self-reported ties, reciprocated ties are more trustworthy than non reciprocated. For example, one study asking college students to report whom they spend time with found that only 58% recalled spending time with each other.

Clans

Actors in the same clan are all connected at n distance, n (or less), and all actors in between are also members of the same clan.

Speaker Notes:

Discussion

Think about a network you are a part of or may be interested in studying and consider the following questions:

1. What groups might exist in this network?

2. How might you go about identifying them?

3. How might this information be useful?

Essential Readings

The following chapters in Carolan (2014) cover the topics introduced in this conceptual overview in much greater depth:

• Chapter 6: Groups and Positions in Complete Networks

• Chapter 7: Measures for Egocentric Network Analysis

In preparation for the Module 3 Code-Along and Case Study, take a look at the #COMMONCORE project by Supovitz et al. (2017).

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Acknowledgements

This work was supported by the National Science Foundation grants DRL-2025090 and DRL-2321128 (ECR:BCSER). Any opinions, findings, and conclusions expressed in this material are those of the authors and do not necessarily reflect the views of the National Science Foundation.

References

Carolan, Brian. 2014. “Social Network Analysis and Education: Theory, Methods & Applications.” https://doi.org/10.4135/9781452270104.

Supovitz, Jonathan, Alan J Daly, Miguel del Fresno, and Christian Kolouch. 2017. “Commoncore Project.” Retrieved April 3: 2019. https://www.hashtagcommoncore.com.