research-article

Efficiently Clustering Very Large Attributed Graphs

Authors:

Alessandro Baroni,

Maurizio Patrignani, and

Salvatore RuggieriAuthors Info & Claims

ASONAM '17: Proceedings of the 2017 IEEE/ACM International Conference on Advances in Social Networks Analysis and Mining 2017

July 2017

Pages 369 - 376

https://doi.org/10.1145/3110025.3110030

Published: 31 July 2017 Publication History

Abstract

Attributed graphs model real networks by enriching their nodes with attributes accounting for properties. Several techniques have been proposed for partitioning these graphs into clusters that are homogeneous with respect to both semantic attributes and to the structure of the graph. However, time and space complexities of state of the art algorithms limit their scalability to medium-sized graphs. We propose SToC (for Semantic-Topological Clustering), a fast and scalable algorithm for partitioning large attributed graphs. The approach is robust, being compatible both with categorical and with quantitative attributes, and it is tailorable, allowing the user to weight the semantic and topological components. Further, the approach does not require the user to guess in advance the number of clusters. SToC relies on well known approximation techniques such as bottom-k sketches, traditional graph-theoretic concepts, and a new perspective on the composition of heterogeneous distance measures. Experimental results demonstrate its ability to efficiently compute high-quality partitions of large scale attributed graphs.

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cover image ACM Conferences

ASONAM '17: Proceedings of the 2017 IEEE/ACM International Conference on Advances in Social Networks Analysis and Mining 2017

July 2017

698 pages

ISBN:9781450349932

DOI:10.1145/3110025

Copyright © 2017 ACM.

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Published: 31 July 2017

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ASONAM '17: Advances in Social Networks Analysis and Mining 2017

July 31 - August 3, 2017

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D’ascenzo AD’emidio M(2023)Top-k Distance Queries on Large Time-Evolving GraphsIEEE Access10.1109/ACCESS.2023.331660211(102228-102242)Online publication date: 2023
https://doi.org/10.1109/ACCESS.2023.3316602
Rostami MOussalah MBerahmand KFarrahi V(2023)Community Detection Algorithms in Healthcare Applications: A Systematic ReviewIEEE Access10.1109/ACCESS.2023.326065211(30247-30272)Online publication date: 2023
https://doi.org/10.1109/ACCESS.2023.3260652
Antunes NBanerjee SBhamidi SPipiras V(2023)Learning attribute and homophily measures through random walksApplied Network Science10.1007/s41109-023-00558-38:1Online publication date: 27-Jun-2023
https://doi.org/10.1007/s41109-023-00558-3
Antunes NBhamidi SPipiras V(2023)Learning Attribute Distributions Through Random WalksComplex Networks and Their Applications XI10.1007/978-3-031-21131-7_2(17-29)Online publication date: 26-Jan-2023
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Chen LGao YHuang XJensen CZheng B(2022)Efficient Distributed Clustering Algorithms on Star-Schema Heterogeneous GraphsIEEE Transactions on Knowledge and Data Engineering10.1109/TKDE.2020.304763134:10(4781-4796)Online publication date: 1-Oct-2022
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