Network Report: A Structured Description for Network Datasets
暂无分享,去创建一个
[1] Duen Horng Chau,et al. Graph Vulnerability and Robustness: A Survey , 2021, IEEE Transactions on Knowledge and Data Engineering.
[2] Oriol Vinyals,et al. Highly accurate protein structure prediction with AlphaFold , 2021, Nature.
[3] A. Hanna,et al. Documenting Computer Vision Datasets: An Invitation to Reflexive Data Practices , 2021, FAccT.
[4] Emily Denton,et al. Towards Accountability for Machine Learning Datasets: Practices from Software Engineering and Infrastructure , 2020, FAccT.
[5] Felix Naumann,et al. Data Preparation , 2020, SIGMOD Rec..
[6] Jacob G. Scott,et al. Exploring complex networks with the ICON R package , 2020, ArXiv.
[7] Danai Koutra,et al. CoDEx: A Comprehensive Knowledge Graph Completion Benchmark , 2020, EMNLP.
[8] Kristian Kersting,et al. TUDataset: A collection of benchmark datasets for learning with graphs , 2020, ArXiv.
[9] J. Leskovec,et al. Open Graph Benchmark: Datasets for Machine Learning on Graphs , 2020, NeurIPS.
[10] Hanna M. Wallach,et al. Co-Designing Checklists to Understand Organizational Challenges and Opportunities around Fairness in AI , 2020, CHI.
[11] Christos Faloutsos,et al. Higher-Order Label Homogeneity and Spreading in Graphs , 2020, WWW.
[12] R. Stuart Geiger,et al. Garbage in, garbage out?: do machine learning application papers in social computing report where human-labeled training data comes from? , 2019, FAT*.
[13] Mazhar Hameed,et al. Data Preparation: A Survey of Commercial Tools , 2020 .
[14] L. Blommaert,et al. The gender gap in job authority: Do social network resources matter? , 2020, Acta Sociologica.
[15] Joseph Fisher. Measuring Social Bias in Knowledge Graph Embeddings , 2019, ArXiv.
[16] H. V. Jagadish,et al. Learning to Answer Complex Questions over Knowledge Bases with Query Composition , 2019, CIKM.
[17] Jianmo Ni,et al. Justifying Recommendations using Distantly-Labeled Reviews and Fine-Grained Aspects , 2019, EMNLP.
[18] William L. Hamilton,et al. Compositional Fairness Constraints for Graph Embeddings , 2019, ICML.
[19] Inioluwa Deborah Raji,et al. Model Cards for Model Reporting , 2018, FAT.
[20] Kush R. Varshney,et al. Increasing Trust in AI Services through Supplier's Declarations of Conformity , 2018, IBM J. Res. Dev..
[21] Emily M. Bender,et al. Data Statements for Natural Language Processing: Toward Mitigating System Bias and Enabling Better Science , 2018, TACL.
[22] Lei Chen,et al. Interference cancelation scheme with variable bandwidth allocation for universal filtered multicarrier systems in 5G networks , 2018, EURASIP J. Wirel. Commun. Netw..
[23] Juan Carlos De Martin,et al. Ethical and Socially-Aware Data Labels , 2018, SIMBig.
[24] Jure Leskovec,et al. Graph Convolutional Policy Network for Goal-Directed Molecular Graph Generation , 2018, NeurIPS.
[25] Jure Leskovec,et al. Graph Convolutional Neural Networks for Web-Scale Recommender Systems , 2018, KDD.
[26] Ahmed Hosny,et al. The Dataset Nutrition Label: A Framework To Drive Higher Data Quality Standards , 2018, Data Protection and Privacy.
[27] Abolfazl Asudeh,et al. A Nutritional Label for Rankings , 2018, SIGMOD Conference.
[28] Ana-Andreea Stoica,et al. Algorithmic Glass Ceiling in Social Networks: The effects of social recommendations on network diversity , 2018, WWW.
[29] Chongcheng Chen,et al. Data quality analysis and cleaning strategy for wireless sensor networks , 2018, EURASIP J. Wirel. Commun. Netw..
[30] Timnit Gebru,et al. Gender Shades: Intersectional Accuracy Disparities in Commercial Gender Classification , 2018, FAT.
[31] J. Michael Herrmann,et al. A Review of No Free Lunch Theorems, and Their Implications for Metaheuristic Optimisation , 2018 .
[32] Bert Huang,et al. Beyond Parity: Fairness Objectives for Collaborative Filtering , 2017, NIPS.
[33] Max Welling,et al. Semi-Supervised Classification with Graph Convolutional Networks , 2016, ICLR.
[34] Christos Faloutsos,et al. CoreScope: Graph Mining Using k-Core Analysis — Patterns, Anomalies and Algorithms , 2016, 2016 IEEE 16th International Conference on Data Mining (ICDM).
[35] Ming-Wei Chang,et al. Semantic Parsing via Staged Query Graph Generation: Question Answering with Knowledge Base , 2015, ACL.
[36] Vincent A. Traag,et al. Detecting communities using asymptotical Surprise , 2015, Physical review. E, Statistical, nonlinear, and soft matter physics.
[37] Michael Gamon,et al. Representing Text for Joint Embedding of Text and Knowledge Bases , 2015, EMNLP.
[38] Pietro Perona,et al. Microsoft COCO: Common Objects in Context , 2014, ECCV.
[39] Ramana Rao Kompella,et al. Network Sampling: From Static to Streaming Graphs , 2012, TKDD.
[40] L. Takac. DATA ANALYSIS IN PUBLIC SOCIAL NETWORKS , 2012 .
[41] Saharon Rosset,et al. Leakage in data mining: formulation, detection, and avoidance , 2011, TKDD.
[42] Arjan Kuijper,et al. Visual Analysis of Large Graphs: State‐of‐the‐Art and Future Research Challenges , 2011, Eurographics.
[43] Emily M. Bender. Linguistic I Ssues in L Anguage Technology Lilt on Achieving and Evaluating Language-independence in Nlp on Achieving and Evaluating Language-independence in Nlp , 2022 .
[44] Charalampos E. Tsourakakis. Counting triangles in real-world networks using projections , 2011, Knowledge and Information Systems.
[45] Athina Markopoulou,et al. On the bias of BFS (Breadth First Search) , 2010, 2010 22nd International Teletraffic Congress (lTC 22).
[46] Olaf Sporns,et al. Complex network measures of brain connectivity: Uses and interpretations , 2010, NeuroImage.
[47] Minas Gjoka,et al. Walking in Facebook: A Case Study of Unbiased Sampling of OSNs , 2010, 2010 Proceedings IEEE INFOCOM.
[48] Yehuda Koren,et al. Collaborative filtering with temporal dynamics , 2009, KDD.
[49] Jean-Loup Guillaume,et al. Fast unfolding of communities in large networks , 2008, 0803.0476.
[50] Krishna P. Gummadi,et al. Measurement and analysis of online social networks , 2007, IMC '07.
[51] Ying Fan,et al. The effect of weight on community structure of networks , 2006, physics/0609218.
[52] J. Leskovec,et al. Graph evolution: Densification and shrinking diameters , 2006, TKDD.
[53] Christos Faloutsos,et al. Sampling from large graphs , 2006, KDD '06.
[54] Carsten Wiuf,et al. Subnets of scale-free networks are not scale-free: sampling properties of networks. , 2005, Proceedings of the National Academy of Sciences of the United States of America.
[55] Larry Wasserman,et al. All of Statistics: A Concise Course in Statistical Inference , 2004 .
[56] Larry Wasserman,et al. All of Statistics , 2004 .
[57] Mark E. J. Newman,et al. The Structure and Function of Complex Networks , 2003, SIAM Rev..
[58] Robert D. Tortora,et al. Sampling: Design and Analysis , 2000 .
[59] Rajeev Motwani,et al. The PageRank Citation Ranking : Bringing Order to the Web , 1999, WWW 1999.
[60] M. Newman,et al. Renormalization Group Analysis of the Small-World Network Model , 1999, cond-mat/9903357.
[61] Duncan J. Watts,et al. Collective dynamics of ‘small-world’ networks , 1998, Nature.
[62] P. V. Marsden,et al. NETWORK DATA AND MEASUREMENT , 1990 .
[63] P. Killworth,et al. INFORMANT ACCURACY IN SOCIAL NETWORK DATA II , 1977 .