collection curated by joecohen https://academictorrents.com/collection/snap @article{, title= {Food-101}, author= {Bossard, Lukas et al., 2014}, abstract= {101 food categories, with 101,000 images; 250 test images and 750 training images per class. The training images were not cleaned. All images were rescaled to have a maximum side length of 512 pixels.}, keywords= {fastai}, terms= {}, license= {}, superseded= {}, url= {} } https://academictorrents.com/download/470791483f8441764d3b01dbc4d22b3aa58ef46f @article{, title= {Texas Road Network}, keywords= {graph}, journal= {}, author= {Jure Leskovec and Andrej Krevl}, year= {}, url= {}, license= {}, abstract= {From http://snap.stanford.edu/data/roadNet-TX.html Dataset information This is a road network of Texas. Intersections and endpoints are represented by nodes, and the roads connecting these intersections or endpoints are represented by undirected edges. Dataset statistics Nodes: 1379917 Edges: 1921660 Nodes in largest WCC: 1351137 (0.979) Edges in largest WCC: 1879201 (0.978) Nodes in largest SCC: 1351137 (0.979) Edges in largest SCC: 1879201 (0.978) Average clustering coefficient: 0.0470 Number of triangles: 82869 Fraction of closed triangles: 0.02091 Diameter (longest shortest path): 1054 90-percentile effective diameter: 6.7e+02 Source (citation) J. Leskovec, K. Lang, A. Dasgupta, M. Mahoney. Community Structure in Large Networks: Natural Cluster Sizes and the Absence of Large Well-Defined Clusters. Internet Mathematics 6(1) 29--123, 2009.}, superseded= {}, terms= {} } https://academictorrents.com/download/224c0ec354dbf703a2cabf00bfcb14b420c5cb90 @article{, title= {Pennsylvania Road Network}, keywords= {graph}, journal= {}, author= {Jure Leskovec and Andrej Krevl}, year= {}, url= {}, license= {}, abstract= {From http://snap.stanford.edu/data/roadNet-PA.html Dataset information This is a road network of Pennsylvania. Intersections and endpoints are represented by nodes, and the roads connecting these intersections or endpoints are represented by undirected edges. Dataset statistics Nodes: 1088092 Edges: 1541898 Nodes in largest WCC: 1087562 (1.000) Edges in largest WCC: 1541514 (1.000) Nodes in largest SCC: 1087562 (1.000) Edges in largest SCC: 1541514 (1.000) Average clustering coefficient: 0.0465 Number of triangles: 67150 Fraction of closed triangles: 0.02062 Diameter (longest shortest path): 786 90-percentile effective diameter: 5.3e+02 Source (citation) J. Leskovec, K. Lang, A. Dasgupta, M. Mahoney. Community Structure in Large Networks: Natural Cluster Sizes and the Absence of Large Well-Defined Clusters. Internet Mathematics 6(1) 29--123, 2009.}, superseded= {}, terms= {} } https://academictorrents.com/download/16a16a4fbf5342d644326a6eef258e5499cf8328 @article{, title= {California Road Network}, keywords= {graph}, journal= {}, author= {Jure Leskovec and Andrej Krevl}, year= {}, url= {}, license= {}, abstract= {From http://snap.stanford.edu/data/roadNet-CA.html Dataset information A road network of California. Intersections and endpoints are represented by nodes and the roads connecting these intersections or road endpoints are represented by undirected edges. Dataset statistics Nodes: 1965206 Edges: 2766607 Nodes in largest WCC: 1957027 (0.996) Edges in largest WCC: 2760388 (0.998) Nodes in largest SCC: 1957027 (0.996) Edges in largest SCC: 2760388 (0.998) Average clustering coefficient: 0.0464 Number of triangles: 120676 Fraction of closed triangles: 0.02097 Diameter (longest shortest path): 849 90-percentile effective diameter: 5e+02 Source (citation) J. Leskovec, K. Lang, A. Dasgupta, M. Mahoney. Community Structure in Large Networks: Natural Cluster Sizes and the Absence of Large Well-Defined Clusters. Internet Mathematics 6(1) 29--123, 2009.}, superseded= {}, terms= {} } https://academictorrents.com/download/0fa73e4f646b3e3258e7af3e22d651a2cf342de7 @article{, title= {Epinions SNAP Social Network Data}, journal= {}, author= {Stanford Network Analysis Platform (SNAP)}, year= {}, url= {}, abstract= {This is a who-trust-whom online social network of a a general consumer review site Epinions.com. Members of the site can decide whether to ''trust'' each other. All the trust relationships interact and form the Web of Trust which is then combined with review ratings to determine which reviews are shown to the user. Dataset statistics Nodes75879 Edges508837 Nodes in largest WCC75877 (1.000) Edges in largest WCC508836 (1.000) Nodes in largest SCC32223 (0.425) Edges in largest SCC443506 (0.872) Average clustering coefficient0.1378 Number of triangles1624481 Fraction of closed triangles0.0229 Diameter (longest shortest path)14 90-percentile effective diameter5 }, keywords= {}, terms= {} } https://academictorrents.com/download/ba43f388cb372f72a91d7c08c54a3f8b36fe3505 @article{, title= {Live Journal SNAP Network Data}, journal= {}, author= {Stanford Network Analysis Platform (SNAP)}, year= {}, url= {}, abstract= {LiveJournal is a free on-line community with almost 10 million members; a significant fraction of these members are highly active. (For example, roughly 300,000 update their content in any given 24-hour period.) LiveJournal allows members to maintain journals, individual and group blogs, and it allows people to declare which other members are their friends they belong. Dataset statistics Nodes4847571 Edges68993773 Nodes in largest WCC4843953 (0.999) Edges in largest WCC68983820 (1.000) Nodes in largest SCC3828682 (0.790) Edges in largest SCC65825429 (0.954) Average clustering coefficient0.2742 Number of triangles285730264 Fraction of closed triangles0.04266 Diameter (longest shortest path)16 90-percentile effective diameter6.5 }, keywords= {}, terms= {} } https://academictorrents.com/download/227d085132908313beb19e9d334bfbdce042a8f6 @article{, title= {Twitter SNAP Network Data}, journal= {}, author= {Stanford Network Analysis Platform (SNAP)}, year= {}, url= {}, abstract= {This dataset consists of 'circles' (or 'lists') from Twitter. Twitter data was crawled from public sources. The dataset includes node features (profiles), circles, and ego networks. Data is also available from Facebook and Google+. ##Dataset statistics |Attribute| Value| |---------|--------| |Nodes|81306| |Edges|1768149| |Nodes in largest WCC|81306 (1.000)| |Edges in largest WCC|1768149 (1.000)| |Nodes in largest SCC|68413 (0.841)| |Edges in largest SCC|1685163 (0.953)| |Average clustering coefficient|0.5653| |Number of triangles|13082506| |Fraction of closed triangles|0.06415| |Diameter (longest shortest path)|7| |90-percentile effective diameter|4.5| }, keywords= {}, terms= {} } https://academictorrents.com/download/276e1028b08decbf711f275a57901dbde88ca5ab @article{, title= {Google Plus SNAP Network Data}, journal= {}, author= {Stanford Network Analysis Platform (SNAP)}, year= {}, url= {}, abstract= {This dataset consists of 'circles' from Google+. Google+ data was collected from users who had manually shared their circles using the 'share circle' feature. The dataset includes node features (profiles), circles, and ego networks. Data is also available from Facebook and Twitter. Dataset statistics Nodes107614 Edges13673453 Nodes in largest WCC107614 (1.000) Edges in largest WCC13673453 (1.000) Nodes in largest SCC69501 (0.646) Edges in largest SCC9168660 (0.671) Average clustering coefficient0.4901 Number of triangles1073677742 Fraction of closed triangles0.6552 Diameter (longest shortest path)6 90-percentile effective diameter3 Source (citation) J. McAuley and J. Leskovec. Learning to Discover Social Circles in Ego Networks. NIPS, 2012.}, keywords= {}, terms= {} } https://academictorrents.com/download/cd595c024206ee0e10ffd607f4a3a19d37eaf83c @article{, title= {Facebook SNAP Network Data}, journal= {}, author= {Stanford Network Analysis Platform (SNAP)}, year= {}, url= {}, abstract= {This dataset consists of 'circles' (or 'friends lists') from Facebook. Facebook data was collected from survey participants using this Facebook app. The dataset includes node features (profiles), circles, and ego networks. Facebook data has been anonymized by replacing the Facebook-internal ids for each user with a new value. Also, while feature vectors from this dataset have been provided, the interpretation of those features has been obscured. For instance, where the original dataset may have contained a feature "political=Democratic Party", the new data would simply contain "political=anonymized feature 1". Thus, using the anonymized data it is possible to determine whether two users have the same political affiliations, but not what their individual political affiliations represent. Data is also available from Google+ and Twitter. Dataset statistics Nodes4039 Edges88234 Nodes in largest WCC4039 (1.000) Edges in largest WCC88234 (1.000) Nodes in largest SCC4039 (1.000) Edges in largest SCC88234 (1.000) Average clustering coefficient0.6055 Number of triangles1612010 Fraction of closed triangles0.2647 Diameter (longest shortest path)8 90-percentile effective diameter4.7 Note that these statistics were compiled by combining the ego-networks, including the ego nodes themselves (along with an edge to each of their friends).}, keywords= {}, terms= {} } https://academictorrents.com/download/3efc53f35d49669b89039f2b4ec9de11ec1d73fd @article{, title= {Twitter Data - NIPS 2012}, journal= {}, author= {J. McAuley and J. Leskovec}, year= {}, url= {http://snap.stanford.edu/data/egonets-Twitter.html}, license= {}, abstract= {This dataset consists of 'circles' (or 'lists') from Twitter. Twitter data was crawled from public sources. The dataset includes node features (profiles), circles, and ego networks. ##Dataset statistics |Attribute|Value| |---------|-------| |Nodes|81306| |Edges|1768149| |Nodes in largest WCC|81306 (1.000)| |Edges in largest WCC|1768149 (1.000)| |Nodes in largest SCC|68413 (0.841)| |Edges in largest SCC|1685163 (0.953)| |Average clustering coefficient|0.5653| |Number of triangles|13082506| |Fraction of closed triangles|0.06415| |Diameter (longest shortest path)|7| |90-percentile effective diameter|4.5| ##Source (citation) J. McAuley and J. Leskovec. Learning to Discover Social Circles in Ego Networks. NIPS, 2012. ##Files: |Attribute|Value| |---------|-------| |nodeId.edges |The edges in the ego network for the node 'nodeId'. Edges are undirected for facebook, and directed (a follows b) for twitter and gplus. The 'ego' node does not appear, but it is assumed that they follow every node id that appears in this file.| |nodeId.circles |The set of circles for the ego node. Each line contains one circle, consisting of a series of node ids. The first entry in each line is the name of the circle.| |nodeId.feat |The features for each of the nodes that appears in the edge file.| |nodeId.egofeat |The features for the ego user.| |nodeId.featnames |The names of each of the feature dimensions. Features are '1' if the user has this property in their profile, and '0' otherwise. This file has been anonymized for facebook users, since the names of the features would reveal private data.|}, keywords= {twitter, social networks, NIPS}, terms= {} } https://academictorrents.com/download/046cf7a75db2a530b1505a4ce125fbe0031f4661