Fault-tolerant concept detection in information networks

Tobias Kötter, Stephan Günnemann, Michael R. Berthold, Christos Faloutsos

Research output: Contribution to journalConference articlepeer-review


Given information about medical drugs and their properties, how can we automatically discover that Aspirin has blood-thinning properties, and thus prevents heart attacks? Expressed in more general terms, if we have a large information network that integrates data from heterogeneous data sources, how can we extract semantic information that provides a better understanding of the integrated data and also helps us to identify missing links? We propose to extract concepts that describe groups of objects and their common properties from the integrated data. The discovered concepts provide semantic information as well as an abstract view on the integrated data and thus improve the understanding of complex systems. Our proposed method has the following desirable properties: (a) it is parameter-free and therefore requires no user-defined parameters (b) it is fault-tolerant, allowing for the detection of missing links and (c) it is scalable, being linear on the input size. We demonstrate the effectiveness and scalability of the proposed method on real, publicly available graphs.

Original languageEnglish
Pages (from-to)410-421
Number of pages12
JournalLecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)
Volume8443 LNAI
Issue numberPART 1
StatePublished - 2014
Externally publishedYes
Event18th Pacific-Asia Conference on Advances in Knowledge Discovery and Data Mining, PAKDD 2014 - Tainan, Taiwan, Province of China
Duration: 13 May 201416 May 2014


Dive into the research topics of 'Fault-tolerant concept detection in information networks'. Together they form a unique fingerprint.

Cite this