Graph Generation

Torque is an English reading comprehension benchmark built on 3.2k news snippets with 21k human-generated questions querying temporal relationships. Source: TORQUE: A Reading Comprehension Dataset of Temporal Ordering Questions

The People in Social Context (PISC) dataset is a dataset that focuses on social relationships. It consists of 22,670 images of 9 types of social relationships. It has annotations for the bounding boxes of all people, as well as the social relationship between all pairs of people in the images. In addition, it also contains occupation annotation. Source: PISC

A novel traffic flow dataset from publicly available web cameras in the suburbs of Chicago, IL. Source: STREETS: A Novel Camera Network Dataset for Traffic Flow

WikiGraphs is a dataset of Wikipedia articles each paired with a knowledge graph, to facilitate the research in conditional text generation, graph generation and graph representation learning. Existing graph-text paired datasets typically contain small graphs and short text (1 or few sentences), thus limiting the capabilities of the models that can be learned on the data. WikiGraphs is collected by pairing each Wikipedia article from the established WikiText-103 benchmark with a subgraph from the Freebase knowledge graph. This makes it easy to benchmark against other state-of-the-art text generative models that are capable of generating long paragraphs of coherent text. Both the graphs and the text data are of significantly larger scale compared to prior graph-text paired datasets.

The Toulouse Road Network dataset describes patches of road maps from the city of Toulouse, represented both as spatial graphs G = (A, X) and as grayscale segmentation images. The TRN dataset contains 111,034 data points (map tiles), of which: 80,357 are in the training set (around 72.4%), 11,679 are in the validation set (around 10.5%), 18,998 are in the test set (around 17.1%). Each tile represents a squared region of side 0.001 degrees of latitude and longitude on the map, which corresponds to a square of around 110 meters. The semantic segmentation of each patch is represented as a 64 × 64 grayscale image. The dataset is generated starting from publicly available data from OpenStreetMap. More details on the dataset characteristic and generation methods are available in our blogpost. Source: https://github.com/davide-belli/toulouse-road-network-dataset Image Source: https://arxiv.org/pdf/1910.14388.pdf