Name
Abstract | ||
|---|---|---|
Due to the scale and complexity of todays’ social networks, it becomes infeasible to mine them with traditional approaches. A possible solution to reduce such scale and complexity is to produce a compact (lossy) version of the network that represents its major properties. This task is known as graph summarization, which is the subject of this research. Our focus is on time-evolving graphs, a more complex scenario where the dynamics of the network also should be taken into account. We address this problem using tensor decomposition, which enables us to capture the multi-way structure of the time-evolving network. This property is unique and is impossible to obtain with other approaches such as matrix factorization. Experimental evaluation on five real world networks implies promising results demonstrating that tensor decomposition is quite useful for summarizing dynamic networks. |
Year | DOI | Venue |
|---|---|---|
2018 | 10.1007/s10618-018-0583-9 | Data Min. Knowl. Discov. |
Keywords | Field | DocType |
Graph summarization,Time-evolving networks,Tensor decomposition | Graph,Data mining,Social network,Lossy compression,Computer science,Matrix decomposition,Graph summarization,Tensor decomposition | Journal |
Volume | Issue | ISSN |
32 | 5 | 1384-5810 |
Citations | PageRank | References |
0 | 0.34 | 24 |
Authors | ||
3 | ||
Name | Order | Citations | PageRank |
|---|---|---|---|
| Sofia da Silva Fernandes | 1 | 1 | 1.37 |
| Hadi Fanaee-T | 2 | 75 | 8.55 |
| João Gama | 3 | 3785 | 271.37 |