Graph Mining: Applications (invited talk)
| Authors | |
|---|---|
| Year of publication | 2016 |
| Type | Article in Proceedings |
| Conference | Proceedings in Informatics and Information Technologies. Bratislava: WIKT & DaZ |
| MU Faculty or unit | |
| Citation | |
| Field | Informatics |
| Keywords | graph mining; network analysis; data mining; classification; anomaly detection; community detection; recommendation |
| Description | Traditional data mining algorithms typically assume data instances to be independent. However, there is a lot of real-world scenarios where relationships between data instances exist and they are principal for data understanding. For example, there are relationships between people in social networks, between chemical elements in chemical compounds, etc. It is difficult or even impossible to express such information in the classical attribute-value representation. Graph mining is an area of data mining that uses a graph representation of data and it allows us to exploit the relationships in the data. The goal of this talk is to present diverse successful applications of graph mining on real-world graphs. |
| Related projects: |