Abstract
Characterizing and generating graphs is essential for modeling Internet and social network, constructing knowledge graph and discovering new chemical compound molecule. However, the non-unique and high-dimensional nature of graphs, as well as complex community structures and global node/edge-dependent relationships, prevent graphs from being generated directly from observed graphs. As a well-known deep learning framework, generative adversarial networks (GANs) provide a feasible way, and have been applied to graph generation. In this paper, we propose PLSGAN, a Power-Law-modified Sequential Generative Adversarial Network to address aforementioned challenges of graph generation. First, PLSGAN coverts graph topology to node-edge sequences by sampling based on biased random walk. Fake sequences are produced by trained generator and assembled to get target graph. Second, power law distribution of node degree is taken into consideration to modify the learning procedure of GANs. Last, PLSGAN is evaluated on various datasets. Experimental results show that PLSGAN can generate graphs with topological features of observed graphs, exhibit strong generalization properties and outperform state-of-the-art methods.
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Acknowledgements
This work is supported by NSFC (NO. U1936206, U1836109) and “the Fundamental Research Funds for the Central Universities”, Nankai University (No. 63201209).
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Bai, Q., Yin, Y., Lian, Y., Zhang, H., Yuan, X. (2020). PLSGAN: A Power-Law-modified Sequential Generative Adversarial Network for Graph Generation. In: Huang, Z., Beek, W., Wang, H., Zhou, R., Zhang, Y. (eds) Web Information Systems Engineering – WISE 2020. WISE 2020. Lecture Notes in Computer Science(), vol 12342. Springer, Cham. https://doi.org/10.1007/978-3-030-62005-9_9
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