collection curated by joecohen https://academictorrents.com/collection/snap 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. https://academictorrents.com/download/470791483f8441764d3b01dbc4d22b3aa58ef46f From 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. https://academictorrents.com/download/224c0ec354dbf703a2cabf00bfcb14b420c5cb90 From 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. https://academictorrents.com/download/16a16a4fbf5342d644326a6eef258e5499cf8328 From 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. https://academictorrents.com/download/0fa73e4f646b3e3258e7af3e22d651a2cf342de7 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 https://academictorrents.com/download/ba43f388cb372f72a91d7c08c54a3f8b36fe3505 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 https://academictorrents.com/download/227d085132908313beb19e9d334bfbdce042a8f6 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| https://academictorrents.com/download/276e1028b08decbf711f275a57901dbde88ca5ab 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. https://academictorrents.com/download/cd595c024206ee0e10ffd607f4a3a19d37eaf83c 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. https://academictorrents.com/download/3efc53f35d49669b89039f2b4ec9de11ec1d73fd 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.| https://academictorrents.com/download/046cf7a75db2a530b1505a4ce125fbe0031f4661