Statistical Analysis of Network Data: Methods and Models

By Eric D. Kolaczyk. 2009

352 citations recorded by the source corpus.

View Paper: https://www.semanticscholar.org/paper/6e6f6c425ea33135158a8b69f54b91fad1de917f

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Citations in the corpus

Potential networks, contagious communities, and understanding social network structure (2013, unknown): “However, the techniques for reconstructing graph properties from sampled data is much smaller; see Chapter 5 in [17] for a survey of such results.”
Statistical clustering of temporal networks through a dynamic stochastic block model (2015, background): “…analysis has become a major ﬁeld of research, with applications as diverse as sociology, ecology, biology, internet, etc. General references on statistical modeling of random graphs include the recent book by Kolaczyk (2009) and the two reviews by Goldenberg et al. (2010) and Snijders (2011).”
The social distributional hypothesis: a pragmatic proxy for homophily in online social networks (2014, unknown): “Many observations of network properties can be explained just by the network's degree distribution (Kolaczyk 2009).”
Social contagion theory: examining dynamic social networks and human behavior (2011, background): “Similar techniques are described elsewhere [36, 56].”
FAST COMMUNITY DETECTION BY SCORE (2012, background): “In light of this, we take a close look on (cid:5) .”
State of the aRt personality research: A tutorial on network analysis of personality data in R (2015, unknown): “This is because, in contrast to purely data analytic models like factor analysis, networks are naturally coupled to dynamics (e.g., see Kolaczyk, 2009): they can evolve, grow, and change over time, with direct consequences for their dynamic behavior.”
Statistical Models for Social Networks (2011, background): “(2009) and actor-oriented models by Koskinen and Edling (2010). Multivariate Exponential Random Graph Models were discussed in Koehly and Pattison (2005), while actor-oriented models for multivariate networks were proposed by Snijders et al.”
How Complex Is Your Classification Problem? (2018, methodology): “Datasets with this type of noise are considered more complex than their clean counterparts, according to the N1 measure, as observed in Lorena et al. (2012) and Garcia et al.”