TY - GEN
T1 - Exploring Citation Networks with Hybrid Tree Pattern Queries
AU - Wu, Xiaoying
AU - Theodoratos, Dimitri
AU - Skoutas, Dimitrios
AU - Lan, Michael
N1 - Publisher Copyright:
© 2020, Springer Nature Switzerland AG.
PY - 2020
Y1 - 2020
N2 - Scientific impact of publications is often measured using citation networks. However, traditional measures typically rely on direct citations only. To fully leverage citation networks for assessing scientific impact, it is necessary to investigate also indirect scientific influence, which is captured by citation paths. Further, the analysis and exploration of citation networks requires the ability to efficiently evaluate expressive queries on them. In this paper, we propose to use hybrid query patterns to query citation networks. These allow for both edge-to-edge and edge-to-path mappings between the query pattern and the graph, thus being able to extract both direct and indirect relationships. To efficiently evaluate hybrid pattern queries on citation graphs, we employ a pattern matching algorithm which exploits graph simulation to prune nodes that do not appear in the final answer. Our experimental results on citation networks show that our method not only allows for more expressive queries but is also efficient and scalable.
AB - Scientific impact of publications is often measured using citation networks. However, traditional measures typically rely on direct citations only. To fully leverage citation networks for assessing scientific impact, it is necessary to investigate also indirect scientific influence, which is captured by citation paths. Further, the analysis and exploration of citation networks requires the ability to efficiently evaluate expressive queries on them. In this paper, we propose to use hybrid query patterns to query citation networks. These allow for both edge-to-edge and edge-to-path mappings between the query pattern and the graph, thus being able to extract both direct and indirect relationships. To efficiently evaluate hybrid pattern queries on citation graphs, we employ a pattern matching algorithm which exploits graph simulation to prune nodes that do not appear in the final answer. Our experimental results on citation networks show that our method not only allows for more expressive queries but is also efficient and scalable.
UR - http://www.scopus.com/inward/record.url?scp=85090094877&partnerID=8YFLogxK
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U2 - 10.1007/978-3-030-55814-7_26
DO - 10.1007/978-3-030-55814-7_26
M3 - Conference contribution
AN - SCOPUS:85090094877
SN - 9783030558130
T3 - Communications in Computer and Information Science
SP - 311
EP - 322
BT - ADBIS, TPDL and EDA 2020 Common Workshops and Doctoral Consortium - International Workshops
A2 - Bellatreche, Ladjel
A2 - Bieliková, Mária
A2 - Boussaïd, Omar
A2 - Darmont, Jérôme
A2 - Catania, Barbara
A2 - Demidova, Elena
A2 - Duchateau, Fabien
A2 - Hall, Mark
A2 - Mercun, Tanja
A2 - Žumer, Maja
A2 - Novikov, Boris
A2 - Papatheodorou, Christos
A2 - Risse, Thomas
A2 - Romero, Oscar
A2 - Sautot, Lucile
A2 - Talens, Guilaine
A2 - Wrembel, Robert
PB - Springer
T2 - 24th East-European Conference on Advances in Databases and Information Systems, ADBIS 2020, the 24th International Conference on Theory and Practice of Digital Libraries, TPDL 2020, and the 16th Workshop on Business Intelligence and Big Data, EDA 2020
Y2 - 25 August 2020 through 27 August 2020
ER -