Network Structure in Virtual Organizations
Organization Science
The Small-World Phenomenon: An Algorithmic Perspective
The Small-World Phenomenon: An Algorithmic Perspective
Network Ties, Reputation, and the Financing of New Ventures
Management Science
A Relational View of Information Seeking and Learning in Social Networks
Management Science
ACM SIGKDD Explorations Newsletter
Toward an interoperable dynamic network analysis toolkit
Decision Support Systems
Mixed Membership Stochastic Blockmodels
The Journal of Machine Learning Research
Identification of influencers - Measuring influence in customer networks
Decision Support Systems
Online social networks in economics
Decision Support Systems
Social and Economic Networks
Statistical Analysis of Network Data: Methods and Models
Statistical Analysis of Network Data: Methods and Models
A Survey of Statistical Network Models
Foundations and Trends® in Machine Learning
A simple model for complex networks with arbitrary degree distribution and clustering
ICML'06 Proceedings of the 2006 conference on Statistical network analysis
Networks, Crowds, and Markets: Reasoning About a Highly Connected World
Networks, Crowds, and Markets: Reasoning About a Highly Connected World
Visualization of Network Concepts: The Impact of Working Memory Capacity Differences
Information Systems Research
The influence of collaborative technology knowledge on advice network structures
Decision Support Systems
AWSM: Allocation of workflows utilizing social network metrics
Decision Support Systems
ProFID: Practical frequent items discovery in peer-to-peer networks
Future Generation Computer Systems
A social network-empowered research analytics framework for project selection
Decision Support Systems
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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.