TY - JOUR
T1 - Natural and Artificial Dynamics in Graphs
T2 - Concept, Progress, and Future
AU - Fu, Dongqi
AU - He, Jingrui
N1 - Publisher Copyright:
Copyright © 2022 Fu and He.
PY - 2022/12/2
Y1 - 2022/12/2
N2 - Graph structures have attracted much research attention for carrying complex relational information. Based on graphs, many algorithms and tools are proposed and developed for dealing with real-world tasks such as recommendation, fraud detection, molecule design, etc. In this paper, we first discuss three topics of graph research, i.e., graph mining, graph representations, and graph neural networks (GNNs). Then, we introduce the definitions of natural dynamics and artificial dynamics in graphs, and the related works of natural and artificial dynamics about how they boost the aforementioned graph research topics, where we also discuss the current limitation and future opportunities.
AB - Graph structures have attracted much research attention for carrying complex relational information. Based on graphs, many algorithms and tools are proposed and developed for dealing with real-world tasks such as recommendation, fraud detection, molecule design, etc. In this paper, we first discuss three topics of graph research, i.e., graph mining, graph representations, and graph neural networks (GNNs). Then, we introduce the definitions of natural dynamics and artificial dynamics in graphs, and the related works of natural and artificial dynamics about how they boost the aforementioned graph research topics, where we also discuss the current limitation and future opportunities.
KW - artificial dynamics
KW - graph mining
KW - graph neural networks
KW - graph representations
KW - natural dynamics
UR - http://www.scopus.com/inward/record.url?scp=85143894441&partnerID=8YFLogxK
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U2 - 10.3389/fdata.2022.1062637
DO - 10.3389/fdata.2022.1062637
M3 - Review article
C2 - 36532844
AN - SCOPUS:85143894441
SN - 2624-909X
VL - 5
JO - Frontiers in Big Data
JF - Frontiers in Big Data
M1 - 1062637
ER -