Graph Mining: Applications (invited talk)

VACULÍK, Karel. Graph Mining: Applications (invited talk). In Mária Bieliková, Ivan Srba. Proceedings in Informatics and Information Technologies. Bratislava: WIKT & DaZ. Bratislava: Nakladatel’stvo STU, 2016, p. 31-34. ISBN 978-80-227-4619-9.

Basic information

• Original name: Graph Mining: Applications (invited talk)
• Name (in English): Graph Mining: Applications (invited talk)
• Authors: VACULÍK, Karel (203 Czech Republic, guarantor, belonging to the institution).
• Edition: Bratislava, Proceedings in Informatics and Information Technologies. Bratislava: WIKT & DaZ, p. 31-34, 4 pp. 2016.
• Publisher: Nakladatel’stvo STU

Other information

• Original language: Czech
• Type of outcome: Proceedings paper
• Field of Study: 10201 Computer sciences, information science, bioinformatics
• Country of publisher: Slovakia
• Confidentiality degree: is not subject to a state or trade secret
• Publication form: printed version "print"
• RIV identification code: RIV/00216224:14330/16:00091614
• Organization unit: Faculty of Informatics
• ISBN: 978-80-227-4619-9
• Keywords in English: graph mining; network analysis; data mining; classification; anomaly detection; community detection; recommendation
• Tags: International impact

Abstract

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.

Abstract (in English)

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.

Links

• MUNI/A/0945/2015, interní kód MU: Name: Rozsáhlé výpočetní systémy: modely, aplikace a verifikace V.

Investor: Masaryk University, Category A