Network Report: A Structured Description for Network Datasets
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Abstract (arXiv)
The rapid development of network science and technologies depends on shareable datasets. Currently, there is no standard practice for reporting and sharing network datasets. Some network dataset providers only share links, while others provide some contexts or basic statistics. As a result, critical information may be unintentionally dropped, and network dataset consumers may misunderstand or overlook critical aspects. Inappropriately using a network dataset can lead to severe consequences (e.g., discrimination) especially when machine learning models on networks are deployed in high-stake domains. Challenges arise as networks are often used across different domains (e.g., network science, physics, etc) and have complex structures. To facilitate the communication between network dataset providers and consumers, we propose network report. A network report is a structured description that summarizes and contextualizes a network dataset. Network report extends the idea of dataset reports (e.g., Datasheets for Datasets) from prior work with network-specific descriptions of the non-i.i.d. nature, demographic information, network characteristics, etc. We hope network reports encourage transparency and accountability in network research and development across different fields.
Key facts. Cited by 1 paper in the map (data through 14 Oct 2026). Builds on 20 of its 67 references that are in the map. First posted on arXiv 8 Jun 2022.
Builds on
20 of 67 references.
References · 20
- 2014Microsoft COCO: Common Objects in Contextcited by 17,608
- 2016Semi-Supervised Classification with Graph Convolutional Networkscited by 11,361
- 2020Open Graph Benchmark: Datasets for Machine Learning on Graphscited by 2,163
- 2018Graph Convolutional Neural Networks for Web-Scale Recommender Systemscited by 1,138
- 2018Model Cards for Model Reportingcited by 1,080
- 2020TUDataset: A collection of benchmark datasets for learning with graphscited by 728
- 2018Graph Convolutional Policy Network for Goal-Directed Molecular Graph Generationcited by 364
- 2020Towards Accountability for Machine Learning Datasets: Practices from Software Engineering and Infrastructurecited by 138
- 2019Compositional Fairness Constraints for Graph Embeddingscited by 126
- 2020CoDEx: A Comprehensive Knowledge Graph Completion Benchmarkcited by 102
- 2018The Dataset Nutrition Label: A Framework To Drive Higher Data Quality Standardscited by 97
- 2017Beyond Parity: Fairness Objectives for Collaborative Filteringcited by 93
8 more
- 2019Garbage In, Garbage Out? Do Machine Learning Application Papers in Social Computing Report Where Human-Labeled Training Data Comes From?cited by 64
- 2018FactSheets: Increasing Trust in AI Services through Supplier's Declarations of Conformitycited by 31
- 2019Measuring Social Bias in Knowledge Graph Embeddingscited by 17
- 2021Graph Vulnerability and Robustness: A Surveycited by 14
- 2018A Nutritional Label for Rankingscited by 10
- 2020Higher-Order Label Homogeneity and Spreading in Graphscited by 9
- 2015Detecting communities using asymptotical Surprisecited by 6
- 2020Exploring complex networks with the ICON R packagecited by 1
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Citing papers per year: 2026: 1.
1 paper.