Name
Abstract | ||
|---|---|---|
Social network analysis can be an important help for military and criminal intelligence analysis. In real world applications, there is seldom complete knowledge about the network of interest - we only have partial and incomplete information about the nodes and networks present. Community detection in networks is an important area of current research in social network analysis with many applications. Finding community structures is however a challenging task and despite significant effort no satisfactory method has been found. Here we study the problem of community detection in noisy and uncertain networks with missing and false edges and propose methods for detecting community structures in them. The method is based on sampling from an ensemble of certain networks that are consistent with the available information about the uncertain networks. |
Year | DOI | Venue |
|---|---|---|
2011 | 10.1109/EISIC.2011.58 | Intelligence and Security Informatics Conference |
Keywords | Field | DocType |
uncertain network,incomplete information,important area,available information,important help,community detection,criminal intelligence analysis,community structure,uncertain networks,certain network,social network analysis,data mining,uncertainty,merging | Data mining,Computer science,Social network analysis,Uncertain data,Sampling (statistics),Artificial intelligence,Merge (version control),Intelligence analysis,Machine learning,Complete information | Conference |
ISBN | Citations | PageRank |
978-0-7695-4406-9 | 7 | 0.51 |
References | Authors | |
4 | 2 | |
Name | Order | Citations | PageRank |
|---|---|---|---|
| Johan Dahlin | 1 | 33 | 5.24 |
| Pontus Svenson | 2 | 149 | 22.31 |