DeepGG: a Deep Graph Generator
Learning distributions of graphs can be used for automatic drug discovery, molecular design, complex network analysis, and much more. We present an improved framework for learning generative models of graphs based on the idea of deep state machines. To learn state transition decisions we use a set of graph and node embedding techniques as memory of the state machine. Our analysis is based on learning the distribution of random graph generators for which we provide statistical tests to determine which properties can be learned and how well the original distribution of graphs is represented. We show that the design of the state machine favors specific distributions. Models of graphs of size up to 150 vertices are learned. Code and parameters are publicly available to reproduce our results.
Code (1)
Tasks
Drug DiscoveryGraph EmbeddingSimilar Papers 제목 키워드 기반
A Geometric Graph-Based Deep Learning Model for Drug-Target Affinity Prediction
In structure-based drug design, accurately estimating the binding affinity between a candidate ligand and its protein receptor is a central challenge. Recent advances in artificial intelligence, particularly deep learnin…
Graph LearningDrug DiscoveryHoldout SetGraph Generators: State of the Art and Open Challenges
The abundance of interconnected data has fueled the design and implementation of graph generators reproducing real-world linking properties, or gauging the effectiveness of graph algorithms, techniques and applications m…
Community DetectionGraph GenerationGenerator-based Graph Generation via Heat Diffusion
Graph generative modelling has become an essential task due to the wide range of applications in chemistry, biology, social networks, and knowledge representation. In this work, we propose a novel framework for generatin…
Graph GenerationData-driven Intra-Autonomous Systems Graph Generator
Accurate modeling of realistic network topologies is essential for evaluating novel Internet solutions. Current topology generators, notably scale-free-based models, fail to capture multiple properties of intra-AS topolo…
ClusteringAgraSSt: Approximate Graph Stein Statistics for Interpretable Assessment of Implicit Graph Generators
We propose and analyse a novel statistical procedure, coined AgraSSt, to assess the quality of graph generators that may not be available in explicit form. In particular, AgraSSt can be used to determine whether a learnt…
Graph Generation