Corpus ID: 443113

Modeling homophily and stochastic equivalence in symmetric relational data

@inproceedings{Hoff2007ModelingHA,
  title={Modeling homophily and stochastic equivalence in symmetric relational data},
  author={Peter D. Hoff},
  booktitle={NIPS},
  year={2007}
}
  • Peter D. Hoff
  • Published in NIPS 7 November 2007
  • Computer Science, Mathematics
This article discusses a latent variable model for inference and prediction of symmetric relational data. The model, based on the idea of the eigenvalue decomposition, represents the relationship between two nodes as the weighted inner-product of node-specific vectors of latent characteristics. This "eigenmodel" generalizes other popular latent variable models, such as latent class and distance models: It is shown mathematically that any latent class or distance model has a representation as an… Expand

Figures, Tables, and Topics from this paper

Generalized Relational Topic Models with Data Augmentation
TLDR
Experimental results demonstrate the significance of three extensions of relational topic models on improving the prediction performance, and the time efficiency can be dramatically improved with a simple fast approximation method. Expand
Universal Latent Space Model Fitting for Large Networks with Edge Covariates
TLDR
Two universal fitting algorithms for networks with edge covariates are presented: one based on nuclear norm penalization and the other based on projected gradient descent, motivated by maximizing the likelihood function for an existing class of inner-product models. Expand
Nonparametric Bayesian matrix factorization for assortative networks
  • M. Zhou
  • Mathematics, Computer Science
  • 2015 23rd European Signal Processing Conference (EUSIPCO)
  • 2015
TLDR
The gamma process edge partition model is described, which links the binary edges of an undirected and unweighted relational network with a latent factor model via the Bernoulli-Poisson link, and uses the gamma process to support a potentially infinite number of latent communities. Expand
Generalized Latent Factor Models for Social Network Analysis
TLDR
This paper proposes a novel model, called generalized latent factor model (GLFM), for social network analysis by enhancing homophily modeling in MLFM, and devise a minorization-maximization (MM) algorithm with linear-time complexity and convergence guarantee to learn the model parameters. Expand
A Latent Space Model for Multilayer Network Data
TLDR
A Bayesian statistical model to simultaneously characterize two or more social networks defined over a common set of actors with a hierarchical prior distribution is proposed, achieving a compromise between dependent and independent networks. Expand
A Review of Latent Space Models for Social Networks
TLDR
This paper discusses in detail several latent space models provided in literature, providing special attention to distance, class, and eigen models in the context of undirected, binary networks. Expand
Exploration of Large Networks with Covariates via Fast and Universal Latent Space Model Fitting
TLDR
Two universal fitting algorithms for networks with edge covariates are presented: one based on nuclear norm penalization and the other based on projected gradient descent, which are fast and scalable to large networks. Expand
Regression of binary network data with exchangeable latent errors
TLDR
The Probit Exchangeable (PX) Model for undirected binary network data is proposed, based on an assumption of exchangeability, which is common to many of the latent variable network models in the literature, and an algorithm for obtaining the maximum likelihood estimator of the PX model is presented. Expand
Marginally Specified Hierarchical Models for Relational Data
We present a unified approach to modelling dyadic relational data, namely that seen in social, biological and technological networks, without restriction to the binary format. The approach involvesExpand
Advancements in latent space network modelling
TLDR
A latent space model for network data in which the interactions occur between sets of the population and, as a motivating example, a coauthorship network in which it is typical for more than two authors to contribute to an article is considered. Expand
...
1
2
3
4
5
...

References

SHOWING 1-10 OF 19 REFERENCES
A latent mixed membership model for relational data
TLDR
This paper proposes a Bayesian model that uses a hierarchy of probabilistic assumptions about the way objects interact with one another in order to learn latent groups, their typical interaction patterns, and the degree of membership of objects to groups. Expand
Latent Space Approaches to Social Network Analysis
Network models are widely used to represent relational information among interacting units. In studies of social networks, recent emphasis has been placed on random graph models where the nodesExpand
Discovering Latent Classes in Relational Data
TLDR
A domain-general framework for learning abstract relational knowledge, which goes beyond previous category-learning models in psychology and applies in two specific domains: learning the structure of kinship systems and learning causal theories. Expand
Bilinear Mixed-Effects Models for Dyadic Data
This article discusses the use of a symmetric multiplicative interaction effect to capture certain types of third-order dependence patterns often present in social networks and other dyadic datasets.Expand
Estimation and Prediction for Stochastic Blockstructures
A statistical approach to a posteriori blockmodeling for digraphs and valued digraphs is proposed. The probability model assumes that the vertices of the digraph are partitioned into severalExpand
Social network analysis - methods and applications
TLDR
This work characterizes networked structures in terms of nodes (individual actors, people, or things within the network) and the ties, edges, or links that connect them. Expand
Interaction network containing conserved and essential protein complexes in Escherichia coli
TLDR
Insight is provided into the function of previously uncharacterized bacterial proteins and the overall topology of a microbial interaction network, the core components of which are broadly conserved across Prokaryota. Expand
Handcock . Latent space approaches to social network analysis
  • J . Amer . Statist . Assoc .
  • 2002
J. Amer. Statist. Assoc
  • J. Amer. Statist. Assoc
  • 2001
...
1
2
...