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Focused clustering and outlier detection in large attributed graphs (2014)
| Venue: | In ACM Special Interest Group on Knowledge Discovery and Data Mining (SIG-KDD |
| Citations: | 4 - 2 self |
BibTeX
@INPROCEEDINGS{Perozzi14focusedclustering,
author = {Bryan Perozzi and Leman Akoglu},
title = {Focused clustering and outlier detection in large attributed graphs},
booktitle = {In ACM Special Interest Group on Knowledge Discovery and Data Mining (SIG-KDD},
year = {2014}
}
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Abstract
Graph clustering and graph outlier detection have been stud-ied extensively on plain graphs, with various applications. Recently, algorithms have been extended to graphs with at-tributes as often observed in the real-world. However, all of these techniques fail to incorporate the user preference into graph mining, and thus, lack the ability to steer algorithms to more interesting parts of the attributed graph. In this work, we overcome this limitation and introduce a novel user-oriented approach for mining attributed graphs. The key aspect of our approach is to infer user preference by the so-called focus attributes through a set of user-provided exemplar nodes. In this new problem setting, clusters and outliers are then simultaneously mined according to this user preference. Specifically, our FocusCO algorithm iden-tifies the focus, extracts focused clusters and detects outliers. Moreover, FocusCO scales well with graph size, since we perform a local clustering of interest to the user rather than global partitioning of the entire graph. We show the effec-tiveness and scalability of our method on synthetic and real-world graphs, as compared to both existing graph clustering and outlier detection approaches.
Keyphrases
user preference graph clustering real-world graph graph mining local clustering focusco algorithm iden-tifies new problem setting key aspect plain graph novel user-oriented approach detection approach user-provided exemplar node interesting part entire graph attributed graph various application so-called focus graph size global partitioning
