Search Results
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.
Beyond Random Walk and Metropolis-Hastings Samplers: Why You Should Not Backtrack for Unbiased Graph Sampling
In this paper, we propose non-backtracking random walk with re-weighting (NBRW-rw) and MH algorithm with delayed acceptance (MHDA) which are theoretically guaranteed to achieve, at almost no additional cost, not only unbiased graph sampling but also higher efficiency (smaller asymptotic variance of the resulting unbiased estimators) than the SRW-rw and the MH algorithm, respectively.
Methodology Data Structures and Algorithms Networking and Internet Architecture Social and Information Networks Data Analysis, Statistics and Probability Physics and Society
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.
Search In Power-Law Networks
Many communication and social networks have power-law link distributions, containing a few nodes which have a very high degree and many with low degree.
SubNets of Scale-Free Networks Are Not Scale-Free: Sampling Properties of Networks
Most studies of networks have only looked at small subsets of the true network.
Reducing Large Internet Topologies for Faster Simulations
In this paper, we develop methods to “sample” a small realistic graph from a large real network.
