How Venture Capital Firms Choose Syndication Partners: The Moderating Effects of Institutional Uncertainty and Investment Preference

ABSTRACT This study investigates how venture capital firms (VCs) choose syndication partners. Exponential random graph models of Chinese VC syndication networks from 2006 to 2013 show that the homophily mechanism does not always determine VCs’ partner selection. In selecting partners, VCs have to strike a balance between reducing uncertainty and mobilizing heterogeneous resources. Therefore, decisions about partners depend on institutional uncertainty and VCs’ investment preferences. While VCs that focus on traditional business in an immature market are more likely to form homogeneous syndications, their peers that prefer to invest in innovative companies and that can rely on a stable market tend to syndicate with heterogeneous partners.

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Modeling venture capital networks in hospitality and tourism entrepreneurial equity financing: An exponential random graph models approach

International Journal of Hospitality Management ◽

10.1016/j.ijhm.2021.102936 ◽

2021 ◽

Vol 95 ◽

pp. 102936

Author(s):

Bing Liu ◽

Chaoliang Luo ◽

Fang Meng ◽

Hui Jiang

Keyword(s):

Venture Capital ◽

Random Graph ◽

Exponential Random Graph Models ◽

Equity Financing ◽

Graph Models ◽

Random Graph Models ◽

Exponential Random Graph ◽

Hospitality And Tourism

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Exponential random graph models for little networks

Social Networks ◽

10.1016/j.socnet.2020.07.005 ◽

2021 ◽

Vol 64 ◽

pp. 225-238

Author(s):

George G. Vega Yon ◽

Andrew Slaughter ◽

Kayla de la Haye

Keyword(s):

Random Graph ◽

Exponential Random Graph Models ◽

Graph Models ◽

Random Graph Models ◽

Exponential Random Graph

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Marginalized Exponential Random Graph Models

Journal of Computational and Graphical Statistics ◽

10.1080/10618600.2012.694750 ◽

2012 ◽

Vol 21 (4) ◽

pp. 883-900 ◽

Cited By ~ 3

Author(s):

Thomas Suesse

Keyword(s):

Random Graph ◽

Exponential Random Graph Models ◽

Graph Models ◽

Random Graph Models ◽

Exponential Random Graph

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Modelling Network Data: An Introduction to Exponential Random Graph Models

Data Analysis and Classification - Studies in Classification, Data Analysis, and Knowledge Organization ◽

10.1007/978-3-642-03739-9_34 ◽

2009 ◽

pp. 297-305

Author(s):

Susanna Zaccarin ◽

Giulia Rivellini

Keyword(s):

Random Graph ◽

Network Data ◽

Exponential Random Graph Models ◽

Graph Models ◽

Random Graph Models ◽

Exponential Random Graph

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The performance of permutations and exponential random graph models when analyzing animal networks

Behavioral Ecology ◽

10.1093/beheco/araa082 ◽

2020 ◽

Vol 31 (5) ◽

pp. 1266-1276 ◽

Cited By ~ 2

Author(s):

Julian C Evans ◽

David N Fisher ◽

Matthew J Silk

Keyword(s):

Social Network ◽

Random Graph ◽

False Positive ◽

False Negative ◽

Error Rates ◽

Network Data ◽

Exponential Random Graph Models ◽

Graph Models ◽

Random Graph Models ◽

Exponential Random Graph

Abstract Social network analysis is a suite of approaches for exploring relational data. Two approaches commonly used to analyze animal social network data are permutation-based tests of significance and exponential random graph models. However, the performance of these approaches when analyzing different types of network data has not been simultaneously evaluated. Here we test both approaches to determine their performance when analyzing a range of biologically realistic simulated animal social networks. We examined the false positive and false negative error rate of an effect of a two-level explanatory variable (e.g., sex) on the number and combined strength of an individual’s network connections. We measured error rates for two types of simulated data collection methods in a range of network structures, and with/without a confounding effect and missing observations. Both methods performed consistently well in networks of dyadic interactions, and worse on networks constructed using observations of individuals in groups. Exponential random graph models had a marginally lower rate of false positives than permutations in most cases. Phenotypic assortativity had a large influence on the false positive rate, and a smaller effect on the false negative rate for both methods in all network types. Aspects of within- and between-group network structure influenced error rates, but not to the same extent. In "grouping event-based" networks, increased sampling effort marginally decreased rates of false negatives, but increased rates of false positives for both analysis methods. These results provide guidelines for biologists analyzing and interpreting their own network data using these methods.

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