Analysis of Sampling Algorithms 
Sampling algorithms are often used to reduce the complexity of analyzing very large graphs, when crawling subsets of large datasets, or to determine the fastest way to propagate information in a network (the 'maximum coverage problem'). To analyze and compare different sampling algorithms, we translate the process of sampling a (complete) network over time into a growing dynamic graph, i.e., starting from an empty graph, visited nodes as well as their edges to already visited nodes are added. Thereby, a dynamic graph is grown that equals the original network after visiting all nodes. 
General Sampling Approach 
When sampling from an existing graph, the procedure is determined by the following aspects: Where to start, how to proceed, how long to proceed, and when to stop.

Basic Sampling Algorithms 
In our analysis, we considered the following 7 basic sampling algorithms. We call them basic because they are rather simple, well known, and have no parameters.

Extended Basic Sampling Algorithms 
We extended some basic algorithms to investigate the influence of small changes to their behavior (e.g., when hitting a deadend).

Parametrized Sampling Algorithms 
Other sampling algorithms have parameters and thereby a more complex behavior depending on the sampled network and the selected parameters.

Analysis results: 
As examples, we investigated the following scenarios:

Maximum Connected Network Coverage Problem  
The Maximum Connected Network Coverage (MCNC) Problem is to find a way (a sampling algorithm) to explore a network such that all nodes are seen as soon as possible. 
Visualization of Sampling algorithms  
To understand how sampling algorithms work, a visualization of the network they create is very helpful and help to distinguish the different algorithms. 
Walking Type  
MCNC results depending on walking type 
Graph Properties measured during Sampling  
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Monotone Sampling of Networks  
Tim Grube, Benjamin Schiller, Thorsten Strufe In: Proceedings of the 2nd International Workshop on Dynamic Networks and Knowledge Discovery (DyNaK 2014) 
Dynamic Properties of Network Samples  
(Bachelor Thesis) Benedict Jahn 
Samplingbased Network Analysis  
(Master Thesis) Tim Grube 
Subsampling of Complex Networks  
(Master Thesis) Kai Rathmann 
DNA.sampling https://github.com/BenjaminSchiller/DNA.sampling  JavaDoc  
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