Multi-MotifGAN (MMGAN): Motif-targeted Graph Generation and Prediction
Autor: | Eli Chien, Olgica Milenkovic, Jianhao Peng, Anuththari Gamage |
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Jazyk: | angličtina |
Rok vydání: | 2019 |
Předmět: |
Social and Information Networks (cs.SI)
FOS: Computer and information sciences Computer Science - Machine Learning Theoretical computer science Computer science Machine Learning (stat.ML) Computer Science - Social and Information Networks 02 engineering and technology Random walk Graph Machine Learning (cs.LG) Network motif Statistics - Machine Learning 020204 information systems 0202 electrical engineering electronic engineering information engineering 020201 artificial intelligence & image processing Pairwise comparison Motif (music) Graph generation Biological network Generative grammar |
Zdroj: | ICASSP |
Popis: | Generative graph models create instances of graphs that mimic the properties of real-world networks. Generative models are successful at retaining pairwise associations in the underlying networks but often fail to capture higher-order connectivity patterns known as network motifs. Different types of graphs contain different network motifs, an example of which are triangles that often arise in social and biological networks. It is hence vital to capture these higher-order structures to simulate real-world networks accurately. We propose Multi-MotifGAN (MMGAN), a motif-targeted Generative Adversarial Network (GAN) that generalizes the benchmark NetGAN approach. The generalization consists of combining multiple biased random walks, each of which captures a different motif structure. MMGAN outperforms NetGAN at creating new graphs that accurately reflect the network motif statistics of input graphs such as Citeseer, Cora and Facebook. |
Databáze: | OpenAIRE |
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