Sampling from large graphs

Given a huge real graph, how can we derive a representative sample? There are many known algorithms to compute interesting measures (shortest paths, centrality, betweenness, etc.), but several of them become impractical for 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. The deeper, underlying question is subtle: how do we measure success? We answer the above questions, and test our answers by thorough experiments on several, diverse datasets, spanning thousands nodes and edges. We consider several sampling methods, propose novel methods to check the goodness of sampling, and develop a set of scaling laws that describe relations between the properties of the original and the sample. In addition to the theoretical contributions, the practical conclusions from our work a...

• Type: Conference
• Year: 2006
• Where: KDD
• Authors: Jure Leskovec, Christos Faloutsos
• Keywords: Data Mining, Evolutionary Graph Patterns, Huge Real Graph, KDD 2006, Large Graph