Efficient Learning-Based Graph Simulation for Temporal Graphs

Graph simulation has recently received a surge of attention in graph processing and analytics. In real-life applications, e.g. social science, biology, and chemistry, many graphs are composed of a series of evolving graphs (i.e., temporal graphs). While most of the existing graph generators focus on...

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Veröffentlicht in:Data engineering S. 251 - 264
Hauptverfasser: Xiang, Sheng, Xu, Chenhao, Cheng, Dawei, Wang, Xiaoyang, Zhang, Ying
Format: Tagungsbericht
Sprache:Englisch
Veröffentlicht: IEEE 19.05.2025
Schlagworte:
Autoencoders
Biology
Generators
Graph Neural Network
Graph neural networks
Graph Simulation
Information technology
Learning systems
Memory management
Social sciences
Surges
Temporal Graphs
Training
ISSN:2375-026X
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Zusammenfassung:Graph simulation has recently received a surge of attention in graph processing and analytics. In real-life applications, e.g. social science, biology, and chemistry, many graphs are composed of a series of evolving graphs (i.e., temporal graphs). While most of the existing graph generators focus on static graphs, the temporal information of the graphs is ignored. In this paper, we focus on simulating temporal graphs, which aim to reproduce the structural and temporal properties of the observed real-life temporal graphs. In this paper, we first give an overview of the existing temporal graph generators, including recently emerged learning-based approaches. Most of these learning-based methods suffer from one of the limitations: low efficiency in training or slow generating, especially for temporal random walk-based methods. Therefore, we propose an efficient learning-based approach to generate graph snapshots, namely temporal graph autoencoder (TGAE). Specifically, we propose an attention-based graph encoder to encode temporal and structural characteristics on sampled ego-graphs. And we proposed an ego-graph decoder that can achieve a good trade-off between simulation quality and efficiency in temporal graph generation. Finally, the experimental evaluation is conducted among our proposed TGAE and representative temporal graph generators on real-life temporal graphs and synthesized graphs. It is reported that our proposed approach outperforms the state-of-the-art temporal graph generators by means of simulation quality and efficiency.
ISSN:2375-026X
DOI:10.1109/ICDE65448.2025.00026