{'fraction_non_alphanumeric': 0.02131782945736434, 'fraction_numerical': 0.02260981912144703, 'mean_word_length': 5.0984251968503935, 'pattern_counts': {'":': 0, '<': 0, '<?xml version=': 0, '>': 0, 'https://': 0, 'lorem ipsum': 0, 'www.': 0, 'xml': 0}, 'pii_count': 0, 'substrings_counts': 0, 'word_list_counts': {'cursed_substrings.json': 0, 'profanity_word_list.json': 0, 'sexual_word_list.json': 0, 'zh_pornsignals.json': 0}}
GraphGT: Machine Learning Datasets for Graph Generation and Transformation

Graph generation has shown great potential in applications like network design and 1 mobility synthesis and is one of the fastest-growing domains in machine learning 2 for graphs. Despite the success of graph generation, the corresponding real-world 3 datasets are few and limited to areas such as molecules and citation networks. To 4 ﬁll the gap, we introduce GraphGT, a large dataset collection for graph generation 5 and transformation problem, which contains 36 datasets from 9 domains across 6 6 subjects. To assist the researchers with better explorations of the datasets, we 7 provide a systemic review and classiﬁcation of the datasets based on research tasks, 8 graph types, and application domains. We have signiﬁcantly (re)processed all the 9 data from different domains to ﬁt the uniﬁed framework of graph generation and 10 transformation problems. In addition, GraphGT provides an easy-to-use graph 11 generation pipeline that simpliﬁes the process for graph data loading, experimental 12 setup and model evaluation. Finally, we compare the performance of popular 13 graph generative models in 16 graph generation and 17 graph transformation 14 datasets, showing the great power of GraphGT in differentiating and evaluating 15 model capabilities and drawbacks. GraphGT has been regularly updated and 16 welcomes inputs from the community. GraphGT is publicly available at https: 17 //graphgt.github.io/ and can also be accessed via an open Python library. 18