## SNAP/Oregon-2

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

Name | Oregon-2 |
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Group | SNAP |

Matrix ID | 2324 |

Num Rows | 11,806 |

Num Cols | 11,806 |

Nonzeros | 65,460 |

Pattern Entries | 65,460 |

Kind | Undirected Graph Sequence |

Symmetric | Yes |

Date | 2001 |

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

Editor | J. Leskovec |

SVD Statistics | |
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Matrix Norm | 7.524069e+01 |

Minimum Singular Value | 0 |

Condition Number | Inf |

Rank | 3,825 |

sprank(A)-rank(A) | |

Null Space Dimension | 7,981 |

Full Numerical Rank? | no |

Download Singular Values | MATLAB |

Download | MATLAB Rutherford Boeing Matrix Market |
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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-2 Dataset information 9 Autonomous systems graphs, 1 per week between March 31 2001 and May 26 2001. Graphs represent AS peering information inferred from Oregon route-views, Looking glass data, and Routing registry, all combined. 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 with lowest number of nodes - 3 31 2001 Nodes 10900 Edges 31180 Nodes in largest WCC 10900 (1.000) Edges in largest WCC 31180 (1.000) Nodes in largest SCC 10900 (1.000) Edges in largest SCC 31180 (1.000) Average clustering coefficient 0.5009 Number of triangles 82856 Fraction of closed triangles 0.03855 Diameter (longest shortest path) 9 90-percentile effective diameter 4.3 Dataset statistics for graph with highest number of nodes - 5 26 2001 Nodes 11461 Edges 32730 Nodes in largest WCC 11461 (1.000) Edges in largest WCC 32730 (1.000) Nodes in largest SCC 11461 (1.000) Edges in largest SCC 32730 (1.000) Average clustering coefficient 0.4943 Number of triangles 89541 Fraction of closed triangles 0.03701 Diameter (longest shortest path) 9 90-percentile effective diameter 4.3 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, Looking glass data, and Routing registry, ... oregon2_010331.txt.gz from March 31 2001 oregon2_010407.txt.gz from April 7 2001 oregon2_010414.txt.gz from April 14 2001 oregon2_010421.txt.gz from April 21 2001 oregon2_010428.txt.gz from April 28 2001 oregon2_010505.txt.gz from May 05 2001 oregon2_010512.txt.gz from May 12 2001 oregon2_010519.txt.gz from May 19 2001 oregon2_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, oregon2_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. |