Graph Community Discovery Algorithms in Neo4j with a Regularization-based Evaluation Metric

Andreas Kanavos, Georgios Drakopoulos, Athanasios Tsakalidis

Abstract

Community discovery is central to social network analysis as it provides a natural way for decomposing a social graph to smaller ones based on the interactions among individuals. Communities do not need to be disjoint and often exhibit recursive structure. The latter has been established as a distinctive characteristic of large social graphs, indicating a modularity in the way humans build societies. This paper presents the implementation of four established community discovery algorithms in the form of Neo4j higher order analytics with the Twitter4j Java API and their application to two real Twitter graphs with diverse structural properties. In order to evaluate the results obtained from each algorithm a regularization-like metric, balancing the global and local graph self-similarity akin to the way it is done in signal processing, is proposed.

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Paper Citation


in Harvard Style

Kanavos A., Drakopoulos G. and Tsakalidis A. (2017). Graph Community Discovery Algorithms in Neo4j with a Regularization-based Evaluation Metric . In Proceedings of the 13th International Conference on Web Information Systems and Technologies - Volume 1: WEBIST, ISBN 978-989-758-246-2, pages 403-410. DOI: 10.5220/0006382104030410


in Bibtex Style

@conference{webist17,
author={Andreas Kanavos and Georgios Drakopoulos and Athanasios Tsakalidis},
title={Graph Community Discovery Algorithms in Neo4j with a Regularization-based Evaluation Metric},
booktitle={Proceedings of the 13th International Conference on Web Information Systems and Technologies - Volume 1: WEBIST,},
year={2017},
pages={403-410},
publisher={SciTePress},
organization={INSTICC},
doi={10.5220/0006382104030410},
isbn={978-989-758-246-2},
}


in EndNote Style

TY - CONF
JO - Proceedings of the 13th International Conference on Web Information Systems and Technologies - Volume 1: WEBIST,
TI - Graph Community Discovery Algorithms in Neo4j with a Regularization-based Evaluation Metric
SN - 978-989-758-246-2
AU - Kanavos A.
AU - Drakopoulos G.
AU - Tsakalidis A.
PY - 2017
SP - 403
EP - 410
DO - 10.5220/0006382104030410