Sampling Social Networks Using Shortest Paths

‏‏‎ ‎ 2020 benedekrozemberczki/littleballoffur

In this paper, we propose to use the concept of shortest path for sampling social networks.

Leveraging History for Faster Sampling of Online Social Networks

‏‏‎ ‎ 2020 benedekrozemberczki/littleballoffur

Random walk fits naturally with this problem because, for most online social networks, the only query we can issue through the interface is to retrieve the neighbors of a given node (i. e., no access to the full graph topology).

On Random Walk Based Graph Sampling

‏‏‎ ‎ 2020 benedekrozemberczki/littleballoffur

In this paper, we first present a comprehensive analysis of the drawbacks of three widely-used random walk based graph sampling algorithms, called re-weighted random walk (RW) algorithm, Metropolis-Hastings random walk (MH) algorithm and maximum-degree random walk (MD) algorithm.

Sampling Community Structure

‏‏‎ ‎ 2020 benedekrozemberczki/littleballoffur

We propose a novel method, based on concepts from expander graphs, to sample communities in networks.

COMMUNITY DETECTION RELATIONAL REASONING

Estimating and Sampling Graphs with Multidimensional Random Walks

‏‏‎ ‎ 2020 benedekrozemberczki/littleballoffur

We show that the proposed sampling method, which we call Frontier sampling, exhibits all of the nice sampling properties of a regular random walk.

Walking in Facebook: A Case Study of Unbiased Sampling of OSNs

‏‏‎ ‎ 2020 benedekrozemberczki/littleballoffur

Our goal in this paper is to obtain a representative (unbiased) sample of Facebook users by crawling its social graph.

Metropolis Algorithms for Representative Subgraph Sampling

‏‏‎ ‎ 2020 benedekrozemberczki/littleballoffur

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.

Graphs over Time: Densification Laws, Shrinking Diameters and Possible Explanations

‎‎‏‏‎ ‎ 2020 benedekrozemberczki/littleballoffur

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.

GRAPH GENERATION

Walking with Perception: Efficient Random Walk Sampling via Common Neighbor Awareness

‏‏‎ ‎ 2020 benedekrozemberczki/littleballoffur

Random walk is widely applied to sample large-scale graphs due to its simplicity of implementation and solid theoretical foundations of bias analysis.

Network Sampling: From Static to Streaming Graphs

‏‏‎ ‎ 2020 benedekrozemberczki/littleballoffur

Network sampling is integral to the analysis of social, information, and biological networks.