InferWiki is a Knowledge Graph Completion (KGC) dataset that improves upon existing benchmarks in inferential ability, assumptions, and patterns.
4 PAPERS • NO BENCHMARKS YET
Wiki-CS is a Wikipedia-based dataset for benchmarking Graph Neural Networks.
73 PAPERS • 2 BENCHMARKS
This is a large-scale dataset of quantum-mechanically calculated properties (DFT level) of crystalline materials for graph representation learning that contains approximately 900k entries (OQM9HK).
2 PAPERS • 3 BENCHMARKS
KdConv is a Chinese multi-domain Knowledge-driven Conversation dataset, grounding the topics in multi-turn conversations to knowledge graphs.
21 PAPERS • NO BENCHMARKS YET
…Spurred by the success of ML in solving combinatorial and graph problems in other domains, there is growing interest in the design of ML-guided logic synthesis tools. OpenABC-D has intermediate and final outputs in the form of 870,000 And-Inverter-Graphs (AIGs) produced from 1500 synthesis runs plus labels such as the optimized node counts, and de-lay.
1 PAPER • NO BENCHMARKS YET
ConvQuestions is the first realistic benchmark for conversational question answering over knowledge graphs. It contains 11,200 conversations which can be evaluated over Wikidata. For suitability to knowledge graphs, questions were constrained to be objective or factoid in nature, but no other restrictive guidelines were set.
15 PAPERS • NO BENCHMARKS YET
…Given a text query and list of molecules without any reference textual information (represented, for example, as SMILES strings, graphs, or other equivalent representations) retrieve the molecule corresponding This requires the integration of two very different types of information: the structured knowledge represented by text and the chemical properties present in molecular graphs.
22 PAPERS • 4 BENCHMARKS
TextWorld KG is a dynamic Knowledge Graph (KG) extraction dataset. It is based on a set of text-based games generated using.
The NCI1 dataset comes from the cheminformatics domain, where each input graph is used as representation of a chemical compound: each vertex stands for an atom of the molecule, and edges between vertices
229 PAPERS • 2 BENCHMARKS
…2021] to generate 2D molecular graphs. We also remove texts with less than 4 words, and crops descriptions with more than 256 words.
3 PAPERS • 1 BENCHMARK
In this work we create a question answering dataset over the DBLP scholarly knowledge graph (KG).
6 PAPERS • NO BENCHMARKS YET
…To build NASBench-101, the authors carefully constructed a compact, yet expressive, search space, exploiting graph isomorphisms to identify 423k unique convolutional architectures.
139 PAPERS • 1 BENCHMARK
…Because of the structured nature of the data, SocNav1 is particularly well-suited to be used to benchmark non-Euclidean machine learning algorithms such as Graph Neural Networks
…and test responses of children aged 9 through 12, as they participate in a robot-mediated human-human collaborative learning activity named JUSThink, where children in teams of two solve a problem on graphs
…It consists of a large corpus of high-resolution satellite imagery and ground truth road network graphs covering the urban core of forty cities across six countries.
37 PAPERS • 2 BENCHMARKS
ParaQA is a question answering (QA) dataset with multiple paraphrased responses for single-turn conversation over knowledge graphs (KG).
5 PAPERS • NO BENCHMARKS YET
…The floorplans are annotated with room outline polygons, doors/windows as line segments, object-icons as axis-aligned bounding boxes, room-door-room connectivity graphs, and photo-room assignments. Generated room-door-room connectivity graphs for floorplans. Annotated all windows, doors, and other wall openings, and associated them with corresponding rooms.
2 PAPERS • 1 BENCHMARK
…The Point missing setting, introduced in Filling the G_ap_s: Multivariate Time Series Imputation by Graph Neural Networks, is a variant for imputation in which 25% of data are masked out uniformly at random
…By analyzing millions of anonymous mobile phone users’ visit trajectories to various places provided by SafeGraph, the daily and weekly dynamic origin-to-destination (O-D) population flows are computed
Yet Another Great Ontology (YAGO) is a Knowledge Graph that augments WordNet with common knowledge facts extracted from Wikipedia, converting WordNet from a primarily linguistic resource to a common knowledge
336 PAPERS • 7 BENCHMARKS
HAM is a dataset for molecular graph partitioning. This dataset contains coarse-grained (CG) mappings of 1206 organic molecules with less than 25 heavy atoms.
…We propose a Conditional Variational Auto-Encoder (CVAE) based on graph convolutions (GCVAE) to learn garment latent spaces.
…In total, we provide three types of supporting profile information: (1) Knowledge Graph (KG) consists of entities with rich attributes, (2) User Profile (UP) is composed of user settings and information
…Open Link Prediction is defined as follows: Given an Open Knowledge Graph and a question consisting of an entity mention and an open relation, predict mentions as answers.
FreebaseQA is a data set for open-domain QA over the Freebase knowledge graph.
14 PAPERS • NO BENCHMARKS YET
…identifying and collecting data on Wikipedia and expanding its analytical potential, after collecting different data from various sources and processing them, we have generated a dedicated Wikipedia Knowledge Graph We share this Knowledge Graph dataset in an open way, aiming to be useful for a wide range of researchers, such as informetricians, sociologists or data scientists.
Bio-decagon is a dataset for polypharmacy side effect identification problem framed as a multirelational link prediction problem in a two-layer multimodal graph/network of two node types: drugs and proteins
31 PAPERS • 1 BENCHMARK
…Most importantly we overcome the limitations of existing probing datasets by (1) having a larger variety of knowledge graph relations, (2) it contains single- and multi-token entities, (3) we use relations
5 PAPERS • 1 BENCHMARK
…TRIPOD is extended in Movie Summarization via Sparse Graph Construction with more movies in the test set (122 now in total) and multimodal features extracted from the full-length movie videos.
11 PAPERS • NO BENCHMARKS YET
…With their familiar format, the explanations are geared towards commonsense reasoners operating on knowledge graphs and serve as a starting point for ongoing work on improving such systems.
3 PAPERS • NO BENCHMARKS YET
…It contains multimodal data (visual data, temporal-graphs and audio) careful-selected from publicly available videos of dancers performing representative movements of the music style and audio data from
…For each image-question pair in the CLEVR dataset, CLEVR-X contains multiple structured textual explanations which are derived from the original scene graphs.
4 PAPERS • 1 BENCHMARK
…Each training and validation image is also associated with scene graph annotations describing the classes and attributes of those objects in the scene, and their pairwise relations.
433 PAPERS • 5 BENCHMARKS
…Each sentence is paired with a graph that represents its whole-sentence meaning in a tree-structure.
27 PAPERS • 2 BENCHMARKS
PointPattern is a graph classification dataset constructed by simple point patterns from statistical mechanics.
2 PAPERS • NO BENCHMARKS YET
…These road networks are used to benchmark routing algorithms on graphs. This LineCoverage-database is made available under the Open Database License: http://opendatacommons.org/licenses/odbl/1.0/.
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.
…Specifically, that authors construct a dialog grammar that is grounded in the scene graphs of the images from the CLEVR dataset.
10 PAPERS • NO BENCHMARKS YET
RadGraph is a dataset of entities and relations in radiology reports based on our novel information extraction schema, consisting of 600 reports with 30K radiologist annotations and 221K reports with 10.5M
38 PAPERS • NO BENCHMARKS YET
Semi-inductive link prediction (LP) in knowledge graphs (KG) is the task of predicting facts for new, previously unseen entities based on context information.
1 PAPER • 1 BENCHMARK
…Each instance is an undirected graph, one edge per line, in the format a b indicating an edge between vertices a and b. Vertices are 1-indexed.
…CLEVR dataset consists of: a training set of 70k images and 700k questions, a validation set of 15k images and 150k questions, a test set of 15k images and 150k questions about objects, answers, scene graphs
577 PAPERS • 2 BENCHMARKS
…exploration of richer machine learning techniques that combine multi-modal features towards applications like trend analysis, paper recommender engines, category prediction, co-citation networks, knowledge graph
0 PAPER • NO BENCHMARKS YET
…Filtering images The first step is focused on filtering images that have meaningful scene graphs and captions. We filtered all the scene graphs that did not contain any edges. images pass this filter. The relationships should be verbs and not contain nouns or pronouns. We filter all scene graphs that contain an edge not tagged as a verb or that the tag is not in an ad-hoc list of allowed non-verb keywords.
1 PAPER • 2 BENCHMARKS
…The datasplits json files contain the indices (0-index) of the train, validation and test sets used in the paper "Equivariant Graph neural networks for fast electron density estimation of molecules, liquids
…The ontology is specified as a hierarchical graph of event categories, covering a wide range of human and animal sounds, musical instruments and genres, and common everyday environmental sounds.
9 PAPERS • 2 BENCHMARKS