3D point cloud data of various household objects for (semantic and graph-based) object category prediction.
Download HERE. If you use this dataset, please cite the following paper:
Graph Kernels for Object Category Prediction in Task-Dependent Robot Grasping. M. Neumann, P. Moreno, L. An- tanas, R. Garnett, and K. Kersting. In Proceedings of the Eleventh Workshop on Mining and Learning with Graphs (MLG–2013), Chicago, US, 2013.
The populated places graph is a subgraph of a labeled graph built from concepts in the DBpedia ontology marked as “populated places.” Each concept is a node in our graph and is backed by a Wikipedia page. We added an undirected edge between two places if one of their corresponding Wikipedia pages links to the other. In the DBpedia ontology populated places are divided into five classes: country, administrative regions, city, town, and village. This graph does not necessarily exhibit homophily; for example, villages (approximately half the dataset) are much more likely to link to countries than to other villages. We built a graph with 1000, 3000, 5000, and 100000 nodes by taking a breadth-first search from first node “Alabama.” We also provie a graph extractor implemented in MATLAB. Download HERE. If you use this dataset, please cite the following paper:
Benchmark Data Sets for Graph Kernels
This graph database contains collected benchmark data sets for the evaluation of graph kernels. The data sets were collected by Kristian Kersting, Nils M. Kriege, Christopher Morris, Petra Mutzel, and Marion Neumann with partial support of the German Science Foundation (DFG) within the Collaborative Research Center SFB 876 “Providing Information by Resource-Constrained Data Analysis”, project A6“Resource-efficient Graph Mining”.