Service interruption on Monday 11 July from 12:30 to 13:00: all the sites of the CCSD (HAL, EpiSciences, SciencesConf, AureHAL) will be inaccessible (network hardware connection).
Skip to Main content Skip to Navigation
Conference papers

Mining Subjectively Interesting Attributed Subgraphs

Anes Bendimerad 1 Ahmad Mel Jefrey Lijffijt Marc Plantevit 1 Céline Robardet 1 Tijl de Bie 
1 DM2L - Data Mining and Machine Learning
LIRIS - Laboratoire d'InfoRmatique en Image et Systèmes d'information
Abstract : Community detection in graphs, data clustering, and local pattern mining are three mature fields of data mining and machine learning. In recent years, attributed subgraph mining is emerging as a new powerful data mining task in the intersection of these areas. Given a graph and a set of attributes for each vertex, attributed subgraph mining aims to find cohesive subgraphs for which (a subset of) the attribute values has exceptional values in some sense. While research on this task can borrow from the three abovementioned fields, the principled integration of graph and attribute data poses two challenges: the definition of a pattern language that is intuitive and lends itself to efficient search strategies, and the formalization of the interestingness of such patterns. We propose an integrated solution to both of these challenges. The proposed pattern language improves upon prior work in being both highly flexible and intuitive. We show how an effective and principled algorithm can enumerate patterns of this language. The proposed approach for quantifying interestingness of patterns of this language is rooted in information theory, and is able to account for prior knowledge on the data. Prior work typically quantifies interestingness based on the cohesion of the subgraph and for the exceptionality of its attributes separately, combining these in a parameterized trade-off. Instead, in our proposal this trade-off is implicitly handled in a principled, parameter-free manner. Extensive empirical results confirm the proposed pattern syntax is intuitive, and the interestingness measure aligns well with actual subjective interestingness.
Document type :
Conference papers
Complete list of metadata

Cited literature [22 references]  Display  Hide  Download
Contributor : Anes Bendimerad Connect in order to contact the contributor
Submitted on : Thursday, March 7, 2019 - 11:30:50 AM
Last modification on : Tuesday, June 1, 2021 - 2:08:09 PM
Long-term archiving on: : Monday, June 10, 2019 - 3:41:29 PM


MLG - Mining Subjectively Inte...
Files produced by the author(s)


  • HAL Id : hal-02060190, version 1


Anes Bendimerad, Ahmad Mel, Jefrey Lijffijt, Marc Plantevit, Céline Robardet, et al.. Mining Subjectively Interesting Attributed Subgraphs. INTERNATIONAL WORKSHOP ON MINING AND LEARNING WITH GRAPHS, held with SIGKDD 2018, Aug 2018, Londres, United Kingdom. ⟨hal-02060190⟩



Record views


Files downloads