Name generators, structural properties and relational data analysis.

1. The future of personal networks:
notes for a research agenda.
Isidro Maya-Jariego, Deniza Alieva, Romina Cachia, Elena González-
Tinoco, Sergio Granados-Chahín, Daniel Holgado, Esperanza
Márquez-López, Andrés Muñoz-Alvis, A. & Fran Santolaya
5th European Conference on Social Networks (EUSN 2021) 7-10
September 2021, Naples, Italy.

2. Looking forward

3. Three consolidated research topics
• Estimation of the size of personal networks.
• Validity and reliability of name generators.
• Descriptive and comparative analysis of structure
and composition.

4. Following
the tradition
of name
generators…
Data collection Data analysis
How to reduce respondent
burden.
Meta-analysis to integrate
the information of samples
of personal networks.
How to maintain motivation
and cognitive effort in web-
based surveys.
Building typologies of
personal networks.
Comparing accuracy of other
perceived versus self-
reported alter data.
Extraction of ego network
properties from one large
network.
Evaluating relations,
positions and resources.
Automated analysis of
separate ego networks (e. g.
UCINET).

5. Innovations
in network
analysis and
visualizations
Visualizations Relating egocentric and
sociocentric network analysis

Presentation of samples of
personal networks according to
comparison dimensions (e. g.
galaxy).

Matching procedures to
rebuild whole networks from
personal networks.

Representation of the abstract
properties of personal
networks (e. g. meta-
visualizations, stratification).

Hybrid designs to explore how
contexts overlap.

Using visualization to gather
new information.

Personal networks types as
attributes in social networks.

Using visualization for
interventions and promoting
changes.

From context name generators
to boundary specification.

Representing longitudinal
changes.

Models of complete networks
applied in personal networks.

Drawings and spontaneous
perception of the respondents.

6. Three main trends…
• Abstract properties of personal networks.
• Key dimensions to compare personal networks.
• Performative uses for data collection and
intervention.

7. References
Bidart, C., Degenne, A., & Grossetti, M. (2018). Personal networks typologies:
A structural approach. Social Networks, 54, 1-11.
Crossley, N., Bellotti, E., Edwards, G., Everett, M. G., Koskinen, J., & Tranmer,
M. (2015). Social network analysis for ego-nets: Social network analysis for
actor-centred networks. Sage.
Giannella, E., & Fischer, C. S. (2016). An inductive typology of egocentric
networks. Social Networks, 47, 15-23.
Maya-Jariego, I. (2021). Building a structural typology of personal networks:
Individual differences in the cohesion of interpersonal environment. Social
Networks, 64, 173-180.
Maya Jariego, I. (2018). Why name generators with a fixed number of alters
may be a pragmatic option for personal network analysis. American Journal of
Community Psychology, 62(1-2), 233-238.
McCarty, C., Lubbers, M. J., Vacca, R., & Molina, J. L. (2019). Conducting
personal network research: A practical guide. Guilford Publications.
Perry, B. L., Pescosolido, B. A., & Borgatti, S. P. (2018). Egocentric network
analysis: Foundations, methods, and models (Vol. 44). Cambridge University
Press.

8. Laboratory of Personal Networks and Communities
University of Seville, Spain