**Matrix: SNAP/Oregon-1**

Description: (9 graphs) AS peering info inferred from Oregon route-views, 3/31-5/26/01

(undirected graph drawing) |

Matrix properties | |

number of rows | 11,492 |

number of columns | 11,492 |

nonzeros | 46,818 |

# strongly connected comp. | 319 |

explicit zero entries | 0 |

nonzero pattern symmetry | symmetric |

numeric value symmetry | symmetric |

type | binary |

structure | symmetric |

Cholesky candidate? | no |

positive definite? | no |

author | J. Leskovec, J. Kleinberg and C. Faloutsos |

editor | J. Leskovec |

date | 2001 |

kind | undirected graph sequence |

2D/3D problem? | no |

Additional fields | size and type |

G | cell 9-by-1 |

Gname | full 9-by-14 |

nodename | full 11492-by-1 |

Notes:

Networks from SNAP (Stanford Network Analysis Platform) Network Data Sets, Jure Leskovec http://snap.stanford.edu/data/index.html email jure at cs.stanford.edu Autonomous systems - Oregon-1 Dataset information 9 graphs of Autonomous Systems (AS) peering information inferred from Oregon route-views between March 31 2001 and May 26 2001. Dataset statistics are calculated for the graph with the lowest (March 31 2001) and highest (from May 26 2001) number of nodes: Dataset statistics for graph witdh lowest number of nodes - 3 31 2001) Nodes 10670 Edges 22002 Nodes in largest WCC 10670 (1.000) Edges in largest WCC 22002 (1.000) Nodes in largest SCC 10670 (1.000) Edges in largest SCC 22002 (1.000) Average clustering coefficient 0.4559 Number of triangles 17144 Fraction of closed triangles 0.009306 Diameter (longest shortest path) 9 90-percentile effective diameter 4.5 Dataset statistics for graph with highest number of nodes - 5 26 2001 Nodes 11174 Edges 23409 Nodes in largest WCC 11174 (1.000) Edges in largest WCC 23409 (1.000) Nodes in largest SCC 11174 (1.000) Edges in largest SCC 23409 (1.000) Average clustering coefficient 0.4532 Number of triangles 19894 Fraction of closed triangles 0.009636 Diameter (longest shortest path) 10 90-percentile effective diameter 4.4 Source (citation) J. Leskovec, J. Kleinberg and C. Faloutsos. Graphs over Time: Densification Laws, Shrinking Diameters and Possible Explanations. ACM SIGKDD International Conference on Knowledge Discovery and Data Mining (KDD), 2005. Files File Description * AS peering information inferred from Oregon route-views ... oregon1_010331.txt.gz from March 31 2001 oregon1_010407.txt.gz from April 7 2001 oregon1_010414.txt.gz from April 14 2001 oregon1_010421.txt.gz from April 21 2001 oregon1_010428.txt.gz from April 28 2001 oregon1_010505.txt.gz from May 05 2001 oregon1_010512.txt.gz from May 12 2001 oregon1_010519.txt.gz from May 19 2001 oregon1_010526.txt.gz from May 26 2001 NOTE: for the UF Sparse Matrix Collection, the primary matrix in this problem set (Problem.A) is the last matrix in the sequence, oregon1_010526, from May 26 2001. The nodes are uniform across all graphs in the sequence in the UF collection. That is, nodes do not come and go. A node that is "gone" simply has no edges. This is to allow comparisons across each node in the graphs. Problem.aux.nodenames gives the node numbers of the original problem. So row/column i in the matrix is always node number Problem.aux.nodenames(i) in all the graphs. Problem.aux.G{k} is the kth graph in the sequence. Problem.aux.Gname(k,:) is the name of the kth graph.

SVD-based statistics: | |

norm(A) | 60.3276 |

min(svd(A)) | 0 |

cond(A) | Inf |

rank(A) | 3,321 |

null space dimension | 8,171 |

full numerical rank? | no |

singular value gap | 7.10717e+11 |

singular values (MAT file): | click here |

SVD method used: | s = svd (full (A)) ; |

status: | ok |

For a description of the statistics displayed above, click here.

*Maintained by Tim Davis, last updated 12-Mar-2014.Matrix pictures by cspy, a MATLAB function in the CSparse package.
Matrix graphs by Yifan Hu, AT&T Labs Visualization Group.
*