## Sybrandt/MOLIERE_2016

MOLIERE: Automatic Biomedical Hypothesis Generation System

Name | MOLIERE_2016 |
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Group | Sybrandt |

Matrix ID | 2810 |

Num Rows | 30,239,687 |

Num Cols | 30,239,687 |

Nonzeros | 6,669,254,694 |

Pattern Entries | 6,677,301,366 |

Kind | Undirected Weighted Graph |

Symmetric | Yes |

Date | 2017 |

Author | J. Sybrandt, M. Shtutman, I. Safro |

Editor | T. Davis |

Download | MATLAB Rutherford Boeing Matrix Market |
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Notes |
MOLIERE Hypothesis Generation Network, J. Sybrandt, Clemson Univ. Justin Sybrandt, jsybran at clemson.edu Matrix Name: MOLIERE_2016 Hypothesis Generation Network Kind/Problem Domain: Term/Document Graph This network was presented in "MOLIERE: Automatic Biomedical Hypothesis Generation System (KDD'17)." Nodes in this network are either MEDLINE documents, UMLS terms, or n-grams we extracted through ToPMine. citation: Justin Sybrandt (Clemson University, Clemson, SC, USA), Michael Shtutman (University of South Carolina, Columbia, SC, USA) Ilya Safro (Clemson University, Clemson, SC, USA), "MOLIERE: Automatic Biomedical Hypothesis Generation System", 23rd ACM SIGKDD Conference on Knowledge Discovery and Data Mining August 13-17, 2017, Halifax, Nova Scotia - Canada https://doi.org/10.1145/3097983.3098057 The original data was 0-based with nodes numbered 0 to n-1. Converted to 1-based for the SuiteSparse Matrix Collection, July 2018. The graph has n=30,239,687 nodes. Node labels are held in the char array Problem.aux.labels. Nodes 1 to 22,281,874 all have labels starting with the capital letter "P", and are PubMed indentification numbers of the MEDLINE documents. Nodes 27,683,534 to n all start with the letter "C", and refer to UMLS terms. Nodes between these two sets of nodes refer to n-grams; none of them start with P or C (or any capital letter). The label of the kth node is also the kth line in the labels text file, and to the kth row and column of the matrix. The graph has 4,023,336 explicit zero edges, which are very important to the problem. They link automatically mined n-grams to UMLS terms representing the same concept. The goal is to find shortest-paths, so an edge of zero-length is important, and not the same as no edge at all. The pattern of the explicit zeros is held in Problem.Zeros in the MATLAB representation, and are part of the files for the Matrix Market and Rutherford-Boeing formats. To operate on the graph G in MATLAB, use G = Problem.A + 1e-100 * Problem.Zeros, or some other suitable tiny value. The nonzero edge weights in the graph range in value between 5e-15 and 3.0. There are no negative edge weights. The graph also has d=3,106,164 duplicate edges; it could be considered a multigraph. However, since the problem is to find shortest paths, the duplicate edges are not needed. The matrix in the SuiteSparse Matrix Collection holds the smallest edge weight for any duplicate. The duplicates not in the matrix are held in Problem.aux.duplicate_edges, as a d-by-3 dense matrix, where each row holds [i j eij] for the edge (i,j) with weight eij. |