Article: A temporal property graph data model compatible with static graphs and its temporal graph query language Journal: International Journal of Sensor Networks (IJSNET) 2026 Vol.50 No.3 pp.186 - 199 Abstract: To address the issues of the disconnection between static and dynamic data in existing temporal graph data models and the insufficient usability of temporal graph query languages, this paper proposes the temporal property graph model compatible with static graphs (TPGMSG), a temporal property graph model compatible with static graphs, along with its query language the temporal graph query language compatible with static graphs (TGQLSG). The model distinguishes between dynamic and static elements, supports the temporal evolution of nodes, edges, and attributes. TGQLSG, as a temporal extension of GQL (the graph query language standard), offers a declarative syntax and comprehensive temporal query capabilities. Furthermore, a Neo4j-based implementation scheme is proposed, where a converter transforms TGQLSG queries into GQL queries. Experimental results show that this model achieves 20.7% and 40.3% improvements in time performance compared to traditional property graphs and static-incompatible models, respectively, while reducing storage space by 79.7%. Inderscience Publishers - linking academia, business and industry through research

Title: A temporal property graph data model compatible with static graphs and its temporal graph query language

Authors: Tiantian Jiang; Guanlin Chen; Haoye Wang; Mingli Song

Addresses: School of Computer and Computing Science, Hangzhou City University, Hangzhou, 310015, China; School of Computer Science, Zhejiang University, Hangzhou, 310012, China ' School of Computer and Computing Science, Hangzhou City University, Hangzhou, 310015, China; School of Computer Science, Zhejiang University, Hangzhou, 310012, China ' School of Computer and Computing Science, Hangzhou City University, Hangzhou, 310015, China ' School of Computer Science, Zhejiang University, Hangzhou, 310012, China

Abstract: To address the issues of the disconnection between static and dynamic data in existing temporal graph data models and the insufficient usability of temporal graph query languages, this paper proposes the temporal property graph model compatible with static graphs (TPGM_SG), a temporal property graph model compatible with static graphs, along with its query language the temporal graph query language compatible with static graphs (TGQL_SG). The model distinguishes between dynamic and static elements, supports the temporal evolution of nodes, edges, and attributes. TGQL_SG, as a temporal extension of GQL (the graph query language standard), offers a declarative syntax and comprehensive temporal query capabilities. Furthermore, a Neo4j-based implementation scheme is proposed, where a converter transforms TGQL_SG queries into GQL queries. Experimental results show that this model achieves 20.7% and 40.3% improvements in time performance compared to traditional property graphs and static-incompatible models, respectively, while reducing storage space by 79.7%.

Keywords: temporal graph; static graph; graph data model; graph query language; sensor.

DOI: 10.1504/IJSNET.2026.152180

International Journal of Sensor Networks, 2026 Vol.50 No.3, pp.186 - 199

Received: 24 Jul 2025
Accepted: 21 Sep 2025
Published online: 10 Mar 2026 *

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Title: A temporal property graph data model compatible with static graphs and its temporal graph query language

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