Analysis and Prediction of Triadic Closure in Online Social Networks

Huang, Hong; Tang, Jie; Liu, Lu; Luo, Jar-Der; Fu, Xiaoming

doi:10.1201/9781315369594-7

Cited by 3 publications

(4 citation statements)

References 1 publication

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“…Research in social networks has explored triadic closure models to explain and predict network evolution over time [8,13]. Detailed studies based on maximumlikelihood models have led to generative models of network evolution in online social networks [11].…”

Section: Related Workmentioning

confidence: 99%

“…Naturally, active engagement between committee members may spawn new contacts; however, preexisting social and organization structures will guide the formation of indirect contacts. It is well known that triads form one of the simplest subunits of group social dynamics [5,7,8,13,15]. Open triads which include committee members may close due to committee deliberation.…”

Section: Committee Deliberation and Network Augmentationmentioning

confidence: 99%

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Modeling the Co-evolution of Committee Formation and Awareness Networks in Organizations

Jones

Friedkin

Singh

2017

Complex Networks &Amp; Their Applications VI

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Large-scale organizations confront a difficult problem of assigning personnel to non-routine tasks. Such projects require novel combinations of individuals from separate parts of the organization. We propose a computational method for assembling a committee that recruits individuals to solve the task. This method is driven by data on interpersonal awareness relationships, fostered by mutual interaction and indirect contact. By inducing a network from these relationships, we can reduce the committee selection problem to the maximum coverage problem. A stochastic model is then presented to capture plausible new relationships which form during committee deliberation. This framework provides administrators the opportunity to track the evolution of awareness relationships while improving the organization's ability to solve non-routine tasks. To track awareness network evolution, we demonstrate correlations in empirical networks between communication network topology and the probability of awareness relationships. To formalize organizational improvement over a series of committee formations, we extend a static committee formation problem to a repeated committee formation problem. After showing the computational cost of exact solutions to this problem, we propose an efficient, heuristic-based solution. Through simulation on real-world networks, we demonstrate that our approach produces more efficient and productive network states than a baseline algorithm during a series of committee formations.

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Section: Related Workmentioning

confidence: 99%

Section: Committee Deliberation and Network Augmentationmentioning

confidence: 99%

Modeling the Co-evolution of Committee Formation and Awareness Networks in Organizations

Jones

Friedkin

Singh

2017

Complex Networks &Amp; Their Applications VI

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“…And leading VC institutions also can act as bridges for resources and investment skills exchange, as they are usually hub nodes in the syndication network [ 19 ]. The ecosystem of VC market is way more complicated than a leader-follower structure, but most previous studies only focus on identifying leading VC institutions [ 22 , 23 , 24 ], and they usually do not have an objective criterion for identifying other groups of VC institutions. For example, Batjargal et al [ 22 ] analyzed empirical data to reveal investment patterns associated with leading VC institutions.…”

Section: Introductionmentioning

confidence: 99%

“…For example, Batjargal et al [ 22 ] analyzed empirical data to reveal investment patterns associated with leading VC institutions. Huang et al [ 23 ] found the triadic closure probability is higher if a leading VC institution is in it. Yet, the above works only used the terminology of “leading VC institutions”, but the classification is determined according to subjective criteria or practical experience from VC practitioners.…”

Section: Introductionmentioning

confidence: 99%

Unveiling Latent Structure of Venture Capital Syndication Networks

Yang

et al. 2022

Entropy

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Venture capital (VC) is a form of private equity financing provided by VC institutions to startups with high growth potential due to innovative technology or novel business models but also high risks. To against uncertainties and benefit from mutual complementarity and sharing resources and information, making joint-investments with other VC institutions on the same startup are pervasive, which forms an ever-growing complex syndication network. Attaining objective classifications of VC institutions and revealing the latent structure of joint-investment behaviors between them can deepen our understanding of the VC industry and boost the healthy development of the market and economy. In this work, we devise an iterative Loubar method based on the Lorenz curve to make objective classification of VC institutions automatically, which does not require setting arbitrary thresholds and the number of categories. We further reveal distinct investment behaviors across categories, where the top-ranked group enters more industries and investment stages with a better performance. Through network embedding of joint investment relations, we unveil the existence of possible territories of top-ranked VC institutions, and the hidden structure of relations between VC institutions.

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Promoting Domain-Specific Participation via Off-topic Forum Participation in Electronic Networks of Practice

Mattson¹,

Davidson²

2018

CAIS

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Analysis and Prediction of Triadic Closure in Online Social Networks

Cited by 3 publications

References 1 publication

Modeling the Co-evolution of Committee Formation and Awareness Networks in Organizations

Modeling the Co-evolution of Committee Formation and Awareness Networks in Organizations

Unveiling Latent Structure of Venture Capital Syndication Networks

Promoting Domain-Specific Participation via Off-topic Forum Participation in Electronic Networks of Practice

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