GraphTune: A Learning-based Graph Generative Model with Tunable Structural Features

Generative models for graphs have been actively studied for decades, and they have a wide range of applications. Recently, learning-based graph generation that reproduces realworld graphs has gradually attracted the attention of many researchers. Several generative models that utilize modern machine learning technologies have been proposed, though a conditional generation of general graphs is less explored in the field. In this paper, we propose a generative model that allows us to tune a value of a global-level structural feature as a condition. Our model called GraphTune enables to tune a value of any structural feature of generated graphs using Long Short Term Memory (LSTM) and Conditional Variational AutoEncoder (CVAE). We performed comparative evaluations of GraphTune and conventional models with a real graph dataset. The evaluations show that GraphTune enables to clearly tune a value of a global-level structural feature compared to the conventional models.

[1]  Jing Liu,et al.  A Dynamic Multiagent Genetic Algorithm for Gene Regulatory Network Reconstruction Based on Fuzzy Cognitive Maps , 2016, IEEE Transactions on Fuzzy Systems.

[2]  Nikos Komodakis,et al.  GraphVAE: Towards Generation of Small Graphs Using Variational Autoencoders , 2018, ICANN.

[3]  Alan M. Frieze,et al.  Random graphs , 2006, SODA '06.

[4]  Duncan J. Watts,et al.  Collective dynamics of ‘small-world’ networks , 1998, Nature.

[5]  A. Vázquez.  Growing network with local rules: preferential attachment, clustering hierarchy, and degree correlations. , 2002, Physical review. E, Statistical, nonlinear, and soft matter physics.

[6]  Krishna P. Gummadi,et al.  On the evolution of user interaction in Facebook , 2009, WOSN '09.

[7]  Jiawei Han,et al.  gSpan: graph-based substructure pattern mining , 2002, 2002 IEEE International Conference on Data Mining, 2002. Proceedings..

[8]  Hosung Park,et al.  What is Twitter, a social network or a news media? , 2010, WWW '10.

[9]  Sho Tsugawa,et al.  Poster: A Tunable Model for Graph Generation Using LSTM and Conditional VAE , 2021, 2021 IEEE 41st International Conference on Distributed Computing Systems (ICDCS).

[10]  Regina Barzilay,et al.  Hierarchical Generation of Molecular Graphs using Structural Motifs , 2020, ICML.

[11]  Tamara G. Kolda,et al.  A Scalable Generative Graph Model with Community Structure , 2013, SIAM J. Sci. Comput..

[12]  Wanxiang Che,et al.  A Neural Transition-Based Approach for Semantic Dependency Graph Parsing , 2018, AAAI.

[13]  Yibo Li,et al.  Multi-objective de novo drug design with conditional graph generative model , 2018, Journal of Cheminformatics.

[14]  M E J Newman,et al.  Finding and evaluating community structure in networks. , 2003, Physical review. E, Statistical, nonlinear, and soft matter physics.

[15]  Eric Jonas,et al.  Deep imitation learning for molecular inverse problems , 2019, NeurIPS.

[16]  Razvan Pascanu,et al.  Learning Deep Generative Models of Graphs , 2018, ICLR 2018.

[17]  Sang-Yeon Hwang,et al.  Scaffold-based molecular design using graph generative model , 2019, ArXiv.

[18]  Jure Leskovec,et al.  GraphRNN: Generating Realistic Graphs with Deep Auto-regressive Models , 2018, ICML.

[19]  Kathryn B. Laskey,et al.  Stochastic blockmodels: First steps , 1983 .

[20]  Hamid R. Rabiee,et al.  Deep Graph Generators: A Survey , 2020, IEEE Access.

[21]  Nicola De Cao,et al.  MolGAN: An implicit generative model for small molecular graphs , 2018, ArXiv.

[22]  Jürgen Schmidhuber,et al.  Long Short-Term Memory , 1997, Neural Computation.

[23]  Sayan Ranu,et al.  GraphGen: A Scalable Approach to Domain-agnostic Labeled Graph Generation , 2020, WWW.

[24]  Xianpei Han,et al.  Sequence-to-Action: End-to-End Semantic Graph Generation for Semantic Parsing , 2018, ACL.

[25]  Xiaojie Guo,et al.  A Systematic Survey on Deep Generative Models for Graph Generation , 2020, ArXiv.

[26]  Yoshua Bengio,et al.  DEFactor: Differentiable Edge Factorization-based Probabilistic Graph Generation , 2018, ArXiv.

[27]  Jean-Loup Guillaume,et al.  Fast unfolding of communities in large networks , 2008, 0803.0476.

[28]  Wenhan Shi,et al.  Conditional Structure Generation through Graph Variational Generative Adversarial Nets , 2019, NeurIPS.

[29]  D. Plenz,et al.  powerlaw: A Python Package for Analysis of Heavy-Tailed Distributions , 2013, PloS one.

[30]  Stephan Günnemann,et al.  NetGAN: Generating Graphs via Random Walks , 2018, ICML.

[31]  Max Welling,et al.  Auto-Encoding Variational Bayes , 2013, ICLR.

[32]  M. De Domenico,et al.  The Anatomy of a Scientific Rumor , 2013, Scientific Reports.

[33]  Shilpa Chakravartula,et al.  Complex Networks: Structure and Dynamics , 2014 .

[34]  L. da F. Costa,et al.  Characterization of complex networks: A survey of measurements , 2005, cond-mat/0505185.

[35]  Albert-László Barabási,et al.  Statistical mechanics of complex networks , 2001, ArXiv.

[36]  Irena Holubová,et al.  Graph Generators: State of the Art and Open Challenges. , 2020 .

[37]  Christos Faloutsos,et al.  R-MAT: A Recursive Model for Graph Mining , 2004, SDM.

[38]  U. Feige,et al.  Spectral Graph Theory , 2015 .