Search Results
Sampling From Large Graphs
Thus graph sampling is essential. The natural questions to ask are (a) which sampling method to use, (b) how small can the sample size be, and (c) how to scale up the measurements of the sample (e. g., the diameter), to get estimates for the large graph.
Spikyball sampling: Exploring large networks via an inhomogeneous filtered diffusion
Studying real-world networks such as social networks or web networks is a challenge.
Graphs over Time: Densification Laws, Shrinking Diameters and Possible Explanations
We provide a new graph generator, based on a "forest fire" spreading process, that has a simple, intuitive justification, requires very few parameters (like the "flammability" of nodes), and produces graphs exhibiting the full range of properties observed both in prior work and in the present study.
Metropolis Algorithms for Representative Subgraph Sampling
While data mining in chemoinformatics studied graph data with dozens of nodes, systems biology and the Internet are now generating graph data with thousands and millions of nodes.
Walking in Facebook: A Case Study of Unbiased Sampling of OSNs
Our goal in this paper is to obtain a representative (unbiased) sample of Facebook users by crawling its social graph.
Sampling Social Networks Using Shortest Paths
In this paper, we propose to use the concept of shortest path for sampling social networks.
Reducing Large Internet Topologies for Faster Simulations
In this paper, we develop methods to “sample” a small realistic graph from a large real network.
Network Sampling: From Static to Streaming Graphs
Network sampling is integral to the analysis of social, information, and biological networks.
Walking with Perception: Efficient Random Walk Sampling via Common Neighbor Awareness
Random walk is widely applied to sample large-scale graphs due to its simplicity of implementation and solid theoretical foundations of bias analysis.
Estimating and Sampling Graphs with Multidimensional Random Walks
We show that the proposed sampling method, which we call Frontier sampling, exhibits all of the nice sampling properties of a regular random walk.
Data Structures and Algorithms Networking and Internet Architecture G.3
