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Network Embedding, also known as "Network Representation Learning", is a collective term for techniques for mapping graph nodes to vectors of real numbers in a multidimensional space. To be useful, a good embedding should preserve the structure of the graph. The vectors can then be used as input to various network and graph analysis tasks, such as link prediction
출처에 같은 태스크 이름으로 연결된 데이터셋입니다. 동명 태스크가 포함될 수 있으므로, 비교할 때는 평가 결과의 조건도 확인하세요.
Arxiv ASTRO-PH (Astro Physics) collaboration network is from the e-print arXiv and covers scientific collaborations between authors papers submitted to Astro Physics category. If an author i co-authored a paper with author j, the graph contains a undirected edge from i to j. If the paper is co-authored by k authors this generates a completely connected (sub)graph on k nodes. Source: https://snap.stanford.edu/data/ca-AstroPh.html
This is a catalogue and repository of network datasets with the aim of aiding scientific research. This website is meant to be browsed both by humans and machines alike, and can also be accessed via a convenient JSON API, or via the graph-tool library. The network datasets themselves are available in several machine-readable formats, in particular gt, GraphML, GML and CSV. The upstream origin of each dataset is meant to be as transparent as possible. Each dataset contains its own publicly available extraction and parsing script, accessible via a git repository, which also includes the entire code for this website, released as Free Software under the AGPLv3. Users are encouraged to inspect the entire pipeline from original upstream data publication, downloading, parsing and format conversion. Users are also welcome to report problems or omissions with the datasets, as well as suggest new ones, either by opening an issue, or simply by forking the git repository and proposing a merge request.
The IS-A dataset is a dataset of relations extracted from a medical ontology. The different entities in the ontology are related by the “is a” relation. For example, ‘acute leukemia’ is a ‘leukemia’. The dataset has 294,693 nodes with 356,541 edges between them. Source: https://arxiv.org/pdf/1906.05939.pdf
The BIRDeep Audio Annotations dataset is a collection of bird vocalizations from Doñana National Park, Spain. It was created as part of the BIRDeep project, which aims to optimize the detection and classification of bird species in audio recordings using deep learning techniques. The dataset is intended for use in training and evaluating models for bird vocalization detection and identification.
The PART-OF dataset is a dataset of relations extracted from a medical ontology. The different entities in the ontology are parts of the human body. The dataset has 16,894 nodes with 19,436 edges between them. Source: https://arxiv.org/pdf/1906.05939.pdf
출처: Papers with Code · CC BY-SA 4.0. 아카이브 자료와 새로 수집한 논문은 별도로 관리합니다.