Name
Abstract | ||
|---|---|---|
Being able to keep the graph scale small while capturing the properties of the original social graph, graph sampling provides an efficient, yet inexpensive solution for social network analysis. The challenge is how to create a small, but representative sample out of the massive social graph with millions or even billions of nodes. Several sampling algorithms have been proposed in previous studies, but there lacks fair evaluation and comparison among them. In this paper, we analyze the state-of art graph sampling algorithms and evaluate their performance on some widely recognized graph properties on directed graphs using large-scale social network datasets. We evaluate not only the commonly used node degree distribution, but also clustering coefficient, which quantifies how well connected are the neighbors of a node in a graph. Through the comparison we have found that none of the algorithms is able to obtain satisfied sampling results in both of these properties, and the performance of each algorithm differs much in different kinds of datasets. |
Year | DOI | Venue |
|---|---|---|
2011 | 10.1109/ICDCSW.2011.34 | ICDCS Workshops |
Keywords | Field | DocType |
original social graph,node degree distribution,state-of art graph,understanding graph sampling algorithms,graph property,graph sampling algorithms,large-scale social network datasets,graph theory,sampling methods,social networking (online),graph sampling,graph scale,massive social graph,social network analysis,social graph,sampling algorithm,algorithm design and analysis,satisfiability,algorithm design,electronic publishing,clustering algorithms,internet,clustering coefficient,degree distribution,encyclopedias,directed graph,social network | Graph theory,Data mining,Social graph,Graph property,Computer science,Algorithm,Directed graph,Theoretical computer science,Degree distribution,Random geometric graph,Clustering coefficient,Graph (abstract data type) | Conference |
ISSN | ISBN | Citations |
1545-0678 E-ISBN : 978-0-7695-4386-4 | 978-0-7695-4386-4 | 26 |
PageRank | References | Authors |
0.94 | 10 | 8 |
Name | Order | Citations | PageRank |
|---|---|---|---|
| Tianyi Wang | 1 | 294 | 27.78 |
| Yang Chen | 2 | 375 | 33.50 |
| Zengbin Zhang | 3 | 536 | 27.05 |
| Tianyin Xu | 4 | 419 | 32.99 |
| Long Jin | 5 | 195 | 5.57 |
| Pan Hui | 6 | 4577 | 309.30 |
| Beixing Deng | 7 | 163 | 18.34 |
| Xing Li | 8 | 698 | 92.13 |