Latent Space Approaches to Social Network Analysis

By Peter D. Hoff, Adrian E. Raftery, Mark S. Handcock. 2002

2,210 citations recorded by the source corpus.

Subfields: Experimental and Cognitive Psychology, Statistical and Nonlinear Physics, Graph Learning

How researchers see this paper

The researchers whose trusted networks rank it highest, strongest advocate first. Every researcher gets a different order.

In Experimental and Cognitive Psychology

In Statistical and Nonlinear Physics

In Graph Learning

Citations in the corpus

node2vec: Scalable Feature Learning for Networks (2016, background): “In particular, prediction tasks on nodes in networks often shuttle between two kinds of similarities: homophily and structural equivalence [12].”
Representation Learning on Graphs: Methods and Applications (2017, background): “We refer the reader to [32], [42], [37], and [7] for comprehensive overviews of these areas.”
A Review of Relational Machine Learning for Knowledge Graphs (2015, background): “[96] proposed this approach first in the context of social networks by modeling the probability of a relationship xij via the score function fðei; ejÞ 1⁄4 dðei; ejÞ where dð ; Þ refers to an arbitrary distance measure such as the Euclidean distance.”
Modeling homophily and stochastic equivalence in symmetric relational data (2007, background): “Results of Hoover [1982] and Aldous [1985, chap.”
Handbook of Big Data (2016, background): “CDV which means “age at the time of the administration of the Autism Diagnostic Observation Schedule,” and “problems due to restricted and repetitive behaviors,” respectively.”
Geom-GCN: Geometric Graph Convolutional Networks (2020, background): “…neural networks effectively address the similar limitations thanks to the stationarity, locality, and compositionality in a continuous space (Bronstein et al., 2017); ii) The notion of network geometry bridges the gap between continuous space and graph (Hoff et al., 2002; Muscoloni et al., 2017).”
Group formation in large social networks: membership, growth, and evolution (2006, neutral): “[18] and Sarkar and Moore [32].”
Dynamic mixed membership blockmodel for evolving networks (2009, background): “Dynamic network tomography can lead to important insights to the robustness of network structures, the cause and consequence of information di.usion, and Appearing in Proceedings of the 26 th International Conference on Machine Learning, Montreal, Canada, 2009.”
Limit theorems for eigenvectors of the normalized Laplacian for random graphs (2016, background): “Random dot product graphs are a specific example of latent position graphs or inhomogeneous random graphs [8, 19], in which each vertex is associated with a latent position Xi and, conditioned on the latent positions, the presence or absence of the edges in the graph are independent Bernoulli random”
MCMCpack: Markov chain Monte Carlo in R (2011, background): “…1998), income dynamics (Geweke and Keane 2000), legislative redistricting (Gelman and King 1990), mass voting (King, Rosen, and Tanner 1999), party competition (Quinn, Martin, and Whitford 1999; Quinn and Martin 2002), and social networks (Hoff, Raftery, and Handcock 2002; Hoff and Ward 2004).”
Inferential Network Analysis with Exponential Random Graph Models (2011, methodology): “Perhaps the most visible approach based on spatial statistics is the latent space network model originally proposed by Hoﬀ, Raftery and Handcock (2002).”
Discriminative Deep Random Walk for Network Classification (2016, background): “One effective strategy to deal with network sparsity is to learn a latent space representation for the entities in a network (Hoff et al., 2002; Zhu, 2012; Tang and Liu, 2011; Tang et al., 2015).”
Mixed Membership Stochastic Blockmodels (2007, methodology): “The posterior inference in latent space models (Hoff et al., 2002; Handcock et al., 2007) is carried out via MCMC sampling, while we have developed a scalable variational inference algorithm to analyze large network structures.”
The analysis of social networks (2008, background): “Latent position models (Hoff et al. 2002) introduce latent quantitative variables.”
Algorithms for estimating relative importance in networks (2003, background): “…`mc sg`s bnohdr ad`q sghr mnshbd `mc sgd etkk bhs`shnm nm sgd fqrs o`fd-Sn bnox nsgdqvhrd+ sn qdotakhrg+ sn onrs nm rdqudqr nq sn qdchrsqhatsd sn khrsr+ qdpthqdr oqhnq rodbhfb odqlhrrhnm `mc.nq ` edd-RHFJCC '/2 @tftrs 13,16+ 1//2 V`rghmfsnm C-B-+ TR@ Bnoxqhfgs 1//2 @BL @BL 0,47002,626,/.”
Efficient discovery of overlapping communities in massive networks (2013, background): “The model captures assortativity — nodes with similar memberships will more likely link to each other (28, 29).”