2021
Tsitseklis, K.; Krommyda, M.; Karyotis, V.; Kantere, V.; Papavassiliou, S.
Scalable Community Detection for Complex Data Graphs via Hyperbolic Network Embedding and Graph Databases Journal Article
In: IEEE Transactions on Network Science and Engineering, vol. 8, no. 2, pp. 1269-1282, 2021, ISSN: 23274697, (cited By 3).
Abstract | Links | BibTeX | Tags: Community detection; Complex data; Database approaches; Diverse fields; Hyperbolic networks; Large datasets; New approaches; Resource description framework, Data visualization, Embeddings; Graph Databases; Large dataset; Population dynamics
@article{Tsitseklis20211269,
title = {Scalable Community Detection for Complex Data Graphs via Hyperbolic Network Embedding and Graph Databases},
author = {K. Tsitseklis and M. Krommyda and V. Karyotis and V. Kantere and S. Papavassiliou},
url = {https://www.scopus.com/inward/record.uri?eid=2-s2.0-85112343198&doi=10.1109%2fTNSE.2020.3022248&partnerID=40&md5=29b1774f2e931454978b20d2dd8d9592},
doi = {10.1109/TNSE.2020.3022248},
issn = {23274697},
year = {2021},
date = {2021-01-01},
journal = {IEEE Transactions on Network Science and Engineering},
volume = {8},
number = {2},
pages = {1269-1282},
publisher = {IEEE Computer Society},
abstract = {Community detection and its variations is one of the typically employed approaches for analyzing graph data originating from various diverse fields. In this paper, we focus on a particular approach for community detection capitalizing on hyperbolic network embedding, which is aimed at analyzing large data graphs. In order to enable its scaling to arbitrary sized data sets and respective data graphs, we extend it by incorporating a graph database approach. This allows for handling a larger number of nodes and edges in the data graph. Also, we turn our focus on the discovery and visualization of communities in Resource Description Framework (RDF) data, namely over linked datasets from diverse areas, explaining how our approach can accommodate relevant analysis. We demonstrate the applicability of the new approach over both real-world and artificially generated datasets showing its feasibility in producing correct results, while being able to scale seamlessly in large datasets. The approach can be used for multi-lateral analysis of feature-rich graph data, originating from diverse sources, enabling the discovery of hidden correlations through the hyperbolic network embedding. © 2013 IEEE.},
note = {cited By 3},
keywords = {Community detection; Complex data; Database approaches; Diverse fields; Hyperbolic networks; Large datasets; New approaches; Resource description framework, Data visualization, Embeddings; Graph Databases; Large dataset; Population dynamics},
pubstate = {published},
tppubtype = {article}
}
Community detection and its variations is one of the typically employed approaches for analyzing graph data originating from various diverse fields. In this paper, we focus on a particular approach for community detection capitalizing on hyperbolic network embedding, which is aimed at analyzing large data graphs. In order to enable its scaling to arbitrary sized data sets and respective data graphs, we extend it by incorporating a graph database approach. This allows for handling a larger number of nodes and edges in the data graph. Also, we turn our focus on the discovery and visualization of communities in Resource Description Framework (RDF) data, namely over linked datasets from diverse areas, explaining how our approach can accommodate relevant analysis. We demonstrate the applicability of the new approach over both real-world and artificially generated datasets showing its feasibility in producing correct results, while being able to scale seamlessly in large datasets. The approach can be used for multi-lateral analysis of feature-rich graph data, originating from diverse sources, enabling the discovery of hidden correlations through the hyperbolic network embedding. © 2013 IEEE.