Network sampling and classification: An investigation of network model representations
01 Jun 2011-Vol. 51, Iss: 3, pp 506-518
TL;DR: It is argued that conclusions based on simulated network studies must focus on the full features of the connectivity patterns of a network instead of on the limited set of network metrics for a specific network type.
Abstract: Methods for generating a random sample of networks with desired properties are important tools for the analysis of social, biological, and information networks. Algorithm-based approaches to sampling networks have received a great deal of attention in recent literature. Most of these algorithms are based on simple intuitions that associate the full features of connectivity patterns with specific values of only one or two network metrics. Substantive conclusions are crucially dependent on this association holding true. However, the extent to which this simple intuition holds true is not yet known. In this paper, we examine the association between the connectivity patterns that a network sampling algorithm aims to generate and the connectivity patterns of the generated networks, measured by an existing set of popular network metrics. We find that different network sampling algorithms can yield networks with similar connectivity patterns. We also find that the alternative algorithms for the same connectivity pattern can yield networks with different connectivity patterns. We argue that conclusions based on simulated network studies must focus on the full features of the connectivity patterns of a network instead of on the limited set of networkmetrics for a specific network type. This fact has important implications for network data analysis: for instance, implications related to the way significance is currently assessed.
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TL;DR: Gupta et al. as discussed by the authors leveraged the newly emerging business analytical capability to rapidly deploy and iterate large-scale, micro-level, in vivo randomized experiments to understand how social influence in networks impacts consumer demand.
Abstract: We leverage the newly emerging business analytical capability to rapidly deploy and iterate large-scale, microlevel, in vivo randomized experiments to understand how social influence in networks impacts consumer demand. Understanding peer influence is critical to estimating product demand and diffusion, creating effective viral marketing, and designing “network interventions” to promote positive social change. But several statistical challenges make it difficult to econometrically identify peer influence in networks. Though some recent studies use experiments to identify influence, they have not investigated the social or structural conditions under which influence is strongest. By randomly manipulating messages sent by adopters of a Facebook application to their 1.3 million peers, we identify the moderating effect of tie strength and structural embeddedness on the strength of peer influence. We find that both embeddedness and tie strength increase influence. However, the amount of physical interaction between friends, measured by coappearance in photos, does not have an effect. This work presents some of the first large-scale in vivo experimental evidence investigating the social and structural moderators of peer influence in networks. The methods and results could enable more effective marketing strategies and social policy built around a new understanding of how social structure and peer influence spread behaviors in society.
This paper was accepted by Alok Gupta, special issue on business analytics.
315 citations
TL;DR: This study examines the impact of global supply network structure on risk diffusion and supply network health and demonstrates the importance of supply network visibility, and indicates that small-world supply network topologies consistently outperform supply networks with scale-free characteristics.
Abstract: Understanding and managing supply chain risks is a critical functional competency for today's global enterprises. A lack of this competency can have significant negative outcomes, including costly production and delivery delays, loss of future sales, and a tarnished corporate image. The ability to identify and mitigate risks, however, is complicated as supply chains are becoming increasingly global, complex, and interconnected. Drawing on the complex systems and epidemiology literature, and using a computational modeling and network analysis approach, we examine the impact of global supply network structure on risk diffusion and supply network health and demonstrate the importance of supply network visibility. Our results show a significant association between network structure and both risk diffusion and supply network health. In particular, our results indicate that small-world supply network topologies consistently outperform supply networks with scale-free characteristics. Theoretically, our study contributes to our understanding of risk management and supply networks as complex networked systems using a computational approach. Managerially, our study illustrates how decision makers can benefit from a network analytic approach to develop a more holistic understanding of system-wide risk diffusion and to guide network governance policies for more favorable health level outcomes. The article concludes by highlighting the main findings and discussing possibilities of future research directions.
152 citations
TL;DR: This research effort investigates the relationship between network characteristics and supply chain resilience and demonstrates that utilizing a reduced list of characteristics yields performance equal to that when using a complete set of characteristics.
86 citations
Proceedings Article•
21 Jun 2014TL;DR: A histogram estimator of a graphon that is provably consistent and numerically efficient is proposed, based on a sorting-and-smoothing (SAS) algorithm, which first sorts the empirical degree of agraph, then smooths the sorted graph using total variation minimization.
Abstract: Exchangeable graph models (ExGM) subsume a number of popular network models. The mathematical object that characterizes an ExGM is termed a graphon. Finding scalable estimators of graphons, provably consistent, remains an open issue. In this paper, we propose a histogram estimator of a graphon that is provably consistent and numerically efficient. The proposed estimator is based on a sorting-and-smoothing (SAS) algorithm, which first sorts the empirical degree of a graph, then smooths the sorted graph using total variation minimization. The consistency of the SAS algorithm is proved by leveraging sparsity concepts from compressed sensing.
82 citations
Cites methods from "Network sampling and classification..."
...Developing statistical models for network data has been a growing research area in statistics and machine learning over the past decade (Goldenberg et al., 2009; Kolaczyk, 2009; Airoldi et al., 2011)....
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01 Nov 2014
TL;DR: The study provides macroscopic view of supply network risk issues across multiple tiers, grounded in theories of supply chains as complex systems, network analysis, and risk management, and demonstrates the importance of visual decision support for supply networkrisk assessment.
Abstract: In today's complex, global supply networks it has become increasingly challenging to identify, evaluate, and mitigate risks of disruption. Traditional supply chain practices have primarily focused on dyadic risk management, rarely considering risks in the sub-tier supply network. However, this approach severely limits a decision maker's ability to understand the highly interconnected nature of systemic risks and develop corresponding mitigation strategies. Grounded in theories of supply chains as complex systems, network analysis, and risk management, we demonstrate the importance of visual decision support for supply network risk assessment. We empirically illustrate our approach with supply network visualization examples from the electronics industry. We conclude the study with implications for the design and implementation of visual supply network decision support systems and future research opportunities. A visual network analytic approach allows mapping of flow, information, and risk.Subtier risk is prevalent in electronics industry and distributions differ by tier.The study provides macroscopic view of supply network risk issues across multiple tiers.Multiple visual depictions reveal distribution of risk levels across supply network.Integrating depictions enables timely identification of dependencies and risks.
56 citations
References
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TL;DR: In this article, the formation of a network is determined by the opposition of two forces: the reproduction of network structure as a general social resource for network members and the alteration of the network structure by entrepreneurs for their own benefit.
Abstract: The formation of a network is determined by the opposition of two forces. The first is the reproduction of network structure as a general social resource for network members. The second is the alteration of network structure by entrepreneurs for their own benefit. The idea of reproduction is a conventional one in organizational sociology but has taken on increased importance due to the work of Bourdieu and Coleman. In contrast, Burt stresses the entrepreneurship of individual agents in exploiting structural holes that lie between constrained positions. Though complementary, the theories of social capital and structural holes have fundamentally different implications for network formation. This paper investigates these theories by examining empirically the formation of the interorganizational network among biotechnology firms. We propose that network structure determines the frequency with which a new biotechnology firm (or startup) establishes new relationships. Network structure indicates both where soci...
1,770 citations
"Network sampling and classification..." refers background in this paper
...Centralized models, such as core-periphery networks, portray interactions in which most individuals report to a central figure and seldom interact with one another [37; 1]....
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TL;DR: This work analyzed a dynamic social network comprising 43,553 students, faculty, and staff at a large university, in which interactions between individuals are inferred from time-stamped e-mail headers recorded over one academic year and are matched with affiliations and attributes.
Abstract: Social networks evolve over time, driven by the shared activities and affiliations of their members, by similarity of individuals' attributes, and by the closure of short network cycles. We analyzed a dynamic social network comprising 43,553 students, faculty, and staff at a large university, in which interactions between individuals are inferred from time-stamped e-mail headers recorded over one academic year and are matched with affiliations and attributes. We found that network evolution is dominated by a combination of effects arising from network topology itself and the organizational structure in which the network is embedded. In the absence of global perturbations, average network properties appear to approach an equilibrium state, whereas individual properties are unstable.
1,713 citations
"Network sampling and classification..." refers background in this paper
...Algorithm-based approaches to sampling networks [40; 34; 3] have received a great deal of attention in recent literature [9; 15; 30; 11; 41]....
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TL;DR: The small-world phenomenon was first investigated as a question in sociology and is a feature of a range of networks arising in nature and technology and is investigated by modelling how individuals can find short chains in a large social network.
Abstract: It is easier to find short chains between points in some networks than others. The small-world phenomenon — the principle that most of us are linked by short chains of acquaintances — was first investigated as a question in sociology1,2 and is a feature of a range of networks arising in nature and technology3,4,5. Experimental study of the phenomenon1 revealed that it has two fundamental components: first, such short chains are ubiquitous, and second, individuals operating with purely local information are very adept at finding these chains. The first issue has been analysed2,3,4, and here I investigate the second by modelling how individuals can find short chains in a large social network.
1,605 citations
TL;DR: It is shown that direct and indirect ties between entrepreneurs and 202 seed-stage investors influence the selection of ventures to fund through a process of information transfer.
Abstract: Explaining how entrepreneurs overcome information asymmetry between themselves and potential investors to obtain financing is an important issue for entrepreneurship research. Our premise is that economic explanations for venture finance, which do not consider how social ties influence this process, are undersocialized and incomplete. However, we also argue that organization theoretic arguments, which draw on the concept of social obligation, are oversocialized. Drawing on the organizational theory literature, and in-depth fieldwork with 50 high-technology ventures, we examine the effects of direct and indirect ties between entrepreneurs and 202 seed-stage investors on venture finance decisions. We show that these ties influence the selection of ventures to fund through a process of information transfer.
1,576 citations
Posted Content•
TL;DR: The mixed membership stochastic block model as discussed by the authors extends block models for relational data to ones which capture mixed membership latent relational structure, thus providing an object-specific low-dimensional representation.
Abstract: Observations consisting of measurements on relationships for pairs of objects arise in many settings, such as protein interaction and gene regulatory networks, collections of author-recipient email, and social networks. Analyzing such data with probabilisic models can be delicate because the simple exchangeability assumptions underlying many boilerplate models no longer hold. In this paper, we describe a latent variable model of such data called the mixed membership stochastic blockmodel. This model extends blockmodels for relational data to ones which capture mixed membership latent relational structure, thus providing an object-specific low-dimensional representation. We develop a general variational inference algorithm for fast approximate posterior inference. We explore applications to social and protein interaction networks.
1,546 citations