article

Multiplicative latent factor models for description and prediction of social networks

Author:

Peter D. HoffAuthors Info & Claims

Computational & Mathematical Organization Theory, Volume 15, Issue 4

Pages 261 - 272

https://doi.org/10.1007/s10588-008-9040-4

Published: 01 December 2009 Publication History

Abstract

We discuss a statistical model of social network data derived from matrix representations and symmetry considerations. The model can include known predictor information in the form of a regression term, and can represent additional structure via sender-specific and receiver-specific latent factors. This approach allows for the graphical description of a social network via the latent factors of the nodes, and provides a framework for the prediction of missing links in network data.

References

[1]

Aldous DJ (1981) Representations for partially exchangeable arrays of random variables. J Multivar Anal 11:581-598.

Crossref

Google Scholar

[2]

Aldous DJ (1985) Exchangeability and related topics. In: École d'été de probabilités de Saint-Flour, XIII- 1983. Lecture notes in math, vol 1117. Springer, Berlin, pp 1-198.

Google Scholar

[3]

Handcock MS (2003) Assessing degeneracy in statistical models of social networks. Working paper no. 39. Center for Statistics and the Social Sciences, University of Washington, Seattle.

Google Scholar

[4]

Hoff PD (2005) Bilinear mixed-effects models for dyadic data. J Am Stat Assoc 100:286-295.

Crossref

Google Scholar

[5]

Hoff PD, Raftery AE, Handcock MS (2002) Latent space approaches to social network analysis. J Am Stat Assoc 97:1090-1098.

Crossref

Google Scholar

[6]

McCullagh P, Nelder JA (1983) Generalized linear models. Monographs on statistics and applied probability. Chapman & Hall, London.

Google Scholar

[7]

Nowicki K, Snijders TAB (2001) Estimation and prediction for stochastic blockstructures. J Am Stat Assoc 96:1077-1087.

Crossref

Google Scholar

[8]

Snijders TAB (2002) Markov chain Monte Carlo estimation of exponential random graph models. J Soc Struct 3.

Google Scholar

[9]

Tierney L (1994) Markov chains for exploring posterior distributions. Ann Stat 22:1701-1762 (with discussion and a rejoinder by the author.).

Crossref

Google Scholar

[10]

Ward MD, Hoff PD (2005) Persistent patterns of international commerce. Working paper no. 45. Center for Statistics and the Social Sciences, University of Washington, Seattle.

Google Scholar

[11]

Warner R, Kenny DA, Stoto M (1979) A new round robin analysis of variance for social interaction data. J Personal Soc Psychol 37:1742-1757.

Crossref

Google Scholar

[12]

Wasserman S, Faust K (1994) Social network analysis: methods and applications. Cambridge University Press, Cambridge.

Google Scholar

[13]

Wasserman S, Pattison P (1996) Logit models and logistic regressions for social networks: I. An introduction to Markov graphs and p*. Psychometrika 61:401-425.

Crossref

Google Scholar

[14]

Wong GY (1982) Round robin analysis of variance via maximum likelihood. J Am Stat Assoc 77:714-724.

Crossref

Google Scholar

Cited By

View all

Singh SMuhuri SMishra SSrivastava DShakya HKumar N(2024)Social Network Analysis: A Survey on Process, Tools, and ApplicationACM Computing Surveys10.1145/364847056:8(1-39)Online publication date: 10-Apr-2024
https://dl.acm.org/doi/10.1145/3648470
Ricci FGuindani MSudderth EKoyejo SMohamed SAgarwal ABelgrave DCho KOh A(2022)Thinned random measures for sparse graphs with overlapping communitiesProceedings of the 36th International Conference on Neural Information Processing Systems10.5555/3600270.3603035(38162-38175)Online publication date: 28-Nov-2022
https://dl.acm.org/doi/10.5555/3600270.3603035
Bock TSchmid AApel S(2021)Measuring and Modeling Group Dynamics in Open-Source Software Development: A Tensor Decomposition ApproachACM Transactions on Software Engineering and Methodology10.1145/347313931:2(1-50)Online publication date: 17-Nov-2021
https://dl.acm.org/doi/10.1145/3473139
Show More Cited By

Recommendations

Analysis of a QR Algorithm for Computing Singular Values

We extend the Golub--Kahan algorithm for computing the singular value decomposition of bidiagonal matrices to triangular matrices $R$. Our algorithm avoids the explicit formation of $R^TR$ or $RR^T$.

We derive a relation between left and right ...
A comprehensive survey of edge prediction in social networks: Techniques, parameters and challenges
Highlights
- Type of social network and formulation of the link prediction problem from their context.
Abstract
Recent development in the area of social networks has sought attention of the researchers to crunch and analyse the data and information of the users to retrieve relevant knowledge for further predictions and recommendations. Edge ...
Analyzing (social media) networks with NodeXL
C&T '09: Proceedings of the fourth international conference on Communities and technologies

We present NodeXL, an extendible toolkit for network overview, discovery and exploration implemented as an add-in to the Microsoft Excel 2007 spreadsheet software. We demonstrate NodeXL data analysis and visualization features with a social media data ...

Comments

Information & Contributors

Information

Published In

cover image Computational & Mathematical Organization Theory

Computational & Mathematical Organization Theory Volume 15, Issue 4

December 2009

92 pages

ISSN:1381-298X

Issue’s Table of Contents

Publisher

Kluwer Academic Publishers

United States

Publication History

Published: 01 December 2009

Author Tags

Qualifiers

Article

Contributors

Other Metrics

View Article Metrics

Bibliometrics & Citations

Bibliometrics

Article Metrics

21
Total Citations
View Citations
0
Total Downloads

Downloads (Last 12 months)0
Downloads (Last 6 weeks)0

Reflects downloads up to 12 Aug 2024

Other Metrics

View Author Metrics

Citations

Cited By

View all

Singh SMuhuri SMishra SSrivastava DShakya HKumar N(2024)Social Network Analysis: A Survey on Process, Tools, and ApplicationACM Computing Surveys10.1145/364847056:8(1-39)Online publication date: 10-Apr-2024
https://dl.acm.org/doi/10.1145/3648470
Ricci FGuindani MSudderth EKoyejo SMohamed SAgarwal ABelgrave DCho KOh A(2022)Thinned random measures for sparse graphs with overlapping communitiesProceedings of the 36th International Conference on Neural Information Processing Systems10.5555/3600270.3603035(38162-38175)Online publication date: 28-Nov-2022
https://dl.acm.org/doi/10.5555/3600270.3603035
Bock TSchmid AApel S(2021)Measuring and Modeling Group Dynamics in Open-Source Software Development: A Tensor Decomposition ApproachACM Transactions on Software Engineering and Methodology10.1145/347313931:2(1-50)Online publication date: 17-Nov-2021
https://dl.acm.org/doi/10.1145/3473139
Ayed FCaron F(2021)Nonnegative Bayesian nonparametric factor models with completely random measuresStatistics and Computing10.1007/s11222-021-10037-331:5Online publication date: 1-Sep-2021
https://dl.acm.org/doi/10.1007/s11222-021-10037-3
McGarry KNelson DAshton M(2020)A Method to Explore the Connectivity Patterns of Proteins and Drugs for Identifying Disease CommunitiesSN Computer Science10.1007/s42979-020-00151-w1:3Online publication date: 23-Apr-2020
https://dl.acm.org/doi/10.1007/s42979-020-00151-w
Jaeger MLippi MPellegrini GPasserini A(2019)Counts-of-counts similarity for prediction and search in relational dataData Mining and Knowledge Discovery10.1007/s10618-019-00621-733:5(1254-1297)Online publication date: 1-Sep-2019
https://dl.acm.org/doi/10.1007/s10618-019-00621-7
Seo EHutchinson RMcIlraith SWeinberger K(2018)Predicting links in plant-pollinator interaction networks using latent factor models with implicit feedbackProceedings of the Thirty-Second AAAI Conference on Artificial Intelligence and Thirtieth Innovative Applications of Artificial Intelligence Conference and Eighth AAAI Symposium on Educational Advances in Artificial Intelligence10.5555/3504035.3504134(808-815)Online publication date: 2-Feb-2018
https://dl.acm.org/doi/10.5555/3504035.3504134
Zhu DCui PZhang ZPei JZhu W(2018)High-Order Proximity Preserved Embedding for Dynamic NetworksIEEE Transactions on Knowledge and Data Engineering10.1109/TKDE.2018.282228330:11(2134-2144)Online publication date: 1-Nov-2018
https://dl.acm.org/doi/10.1109/TKDE.2018.2822283
Robles-Granda PMoreno SNeville J(2017)Unified representation and lifted sampling for generative models of social networksProceedings of the 26th International Joint Conference on Artificial Intelligence10.5555/3172077.3172420(3798-3806)Online publication date: 19-Aug-2017
https://dl.acm.org/doi/10.5555/3172077.3172420
Ou MCui PPei JZhang ZZhu WKrishnapuram BShah MSmola AAggarwal CShen DRastogi R(2016)Asymmetric Transitivity Preserving Graph EmbeddingProceedings of the 22nd ACM SIGKDD International Conference on Knowledge Discovery and Data Mining10.1145/2939672.2939751(1105-1114)Online publication date: 13-Aug-2016
https://dl.acm.org/doi/10.1145/2939672.2939751
Show More Cited By

View Options

View options

Get Access

Login options

Check if you have access through your login credentials or your institution to get full access on this article.

Abstract

References

Cited By

Recommendations

Analysis of a QR Algorithm for Computing Singular Values

A comprehensive survey of edge prediction in social networks: Techniques, parameters and challenges

Analyzing (social media) networks with NodeXL

Comments

Information

Published In

Publisher

Publication History

Author Tags

Qualifiers

Contributors

Other Metrics

Bibliometrics

Article Metrics

Other Metrics

Citations

Cited By

View options

Get Access

Login options

Full Access

Figures

Other

Share

Share this Publication link

Share on social media

Affiliations