{SNAP Datasets}: {Stanford} Large Network Dataset Collection

By Jure Leskovec, A. Krevl. 2014

4,335 citations recorded by the source corpus.

Subfields: Graph Learning, Evaluation & Benchmarks

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Citations in the corpus

Natural-Parameter Networks: A Class of Probabilistic Neural Networks (2016, background): “The third one is from arXiv as one of the SNAP datasets [15].”
Graph Embedding Techniques, Applications, and Performance: A Survey (2017, methodology): “ASTRO-PH [95]: This is a collaboration network of authors of papers submitted to e-print arXiv during the period from January 1993 to April 2003.”
OuterSPACE: An Outer Product Based Sparse Matrix Multiplication Accelerator (2018, methodology): “The real-world matrices we evaluate are derived from the University of Florida SuiteSparse Matrix Collection [21] and the Stanford Network Analysis Project (SNAP) [39], containing a wide spectrum of real-world sparse matrices from diverse domains such as structural engineering, computational fluid d”
Optimizing Ad Allocation in Social Advertising (2015, methodology): “We ran our experiments on four real-world benchmark social networks in the literature of viral marketing: Epinions, Slashdot, Pokec and LiveJournal, all are publicly available at [31].”
The Limits of Popularity-Based Recommendations, and the Role of Social Ties (2016, methodology): “tarting share of products. Let us now describe the experimental setup. 4.1 Experimental setup We run our experiments on six following real-world graphs. Google+ The ego-Gplus graph from SNAP Datasets [21]. It contains 107;614 nodes (users) and 13;673;453 arcs representing a follower-to-followed rela”
Deep Learning for Extreme Multi-label Text Classification (2017, positive): “We used six benchmark datasets, including two small-scale datasets RCV1 [29] (103 labels) and EUR-Lex [31] (3865 labels), two mediumscale datasets Amazon-12K [30] (12,277 labels) and Wiki-30K [51] (29,947 labels), and two large-scale datasets Amazon-670K [28] (670,091 labels) and Wiki-500K1 (501,069”
Link Prediction Based on Graph Neural Networks (2018, methodology): “We further conduct experiments with the setting of node2vec [23] on five networks: arXiv (18,722 nodes and 198,110 edges) [47], Facebook (4,039 nodes and 88,234 edges) [47], BlogCatalog (10,312 nodes, 333,983 edges and 39 attributes) [48], Wikipedia (4,777 nodes, 184,812 edges and 40 attributes) [49”
Non-monotone Submodular Maximization with Nearly Optimal Adaptivity and Query Complexity (2018, positive): “We consider the top 5,000 communities of the YouTube network (Leskovec & Krevl, 2014) and subsample the graph by restricting to 25 randomly chosen communities (Balkanski et al., 2018).”
Transformer Hawkes Process (2020, background): “• StackOverﬂow (Leskovec and Krevl, 2014): StackOverﬂow is a question-answering website.”
Bounding User Contributions: A Bias-Variance Trade-off in Differential Privacy (2019, background): “Power laws are extremely common in real datasets across diverse domains, whether counting the number of movies rated by a user (Harper & Konstan, 2015) or the number of connections a user has in a social network (Leskovec & Krevl, 2014).”
Learning from Your Network of Friends: A Trajectory Representation Learning Model Based on Online Social Ties (2016, neutral): “and Brightkite (BK), both publicly available [10].”
SNAP: A General Purpose Network Analysis and Graph Mining Library (2016, methodology): “As part of SNAP, we are also maintaining and making publicly available the Stanford Large Network Dataset Collection [Leskovec and Krevl 2014], a set of around 80 different social and information real-world networks and datasets from a wide range of domains, including social networks, citation and…”