## SNAP/soc-sign-bitcoin-alpha

SNAP network: Bitcoin Alpha trust weighted signed network

Name | soc-sign-bitcoin-alpha |
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Group | SNAP |

Matrix ID | 2790 |

Num Rows | 3,783 |

Num Cols | 3,783 |

Nonzeros | 24,186 |

Pattern Entries | 24,186 |

Kind | Directed Weighted Temporal Graph |

Symmetric | No |

Date | 2016 |

Author | S. Kumar, F. Spezzano, V.S. Subrahmanian, C. Faloutsos |

Editor | J. Leskovec |

Download | MATLAB Rutherford Boeing Matrix Market |
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Notes |
SNAP (Stanford Network Analysis Platform) Large Network Dataset Collection, Jure Leskovec and Anrej Krevl, http://snap.stanford.edu/data, June 2014. email: jure at cs.stanford.edu Bitcoin Alpha trust weighted signed network https://snap.stanford.edu/data/soc-sign-bitcoin-alpha.html Dataset information This is who-trusts-whom network of people who trade using Bitcoin on a platform called Bitcoin Alpha (http://www.btcalpha.com/). Since Bitcoin users are anonymous, there is a need to maintain a record of users' reputation to prevent transactions with fraudulent and risky users. Members of Bitcoin Alpha rate other members in a scale of -10 (total distrust) to +10 (total trust) in steps of 1. This is the first explicit weighted signed directed network available for research. Dataset statistics Nodes 3,783 Edges 24,186 Range of edge weight -10 to +10 Percentage of positive edges 93% Similar network from another Bitcoin platform, Bitcoin OTC, is available at https://snap.stanford.edu/data/soc-sign-bitcoinotc.html (and as SNAP/bitcoin-otc in the SuiteSparse Matrix Collection). Source (citation) Please cite the following paper if you use this dataset: S. Kumar, F. Spezzano, V.S. Subrahmanian, C. Faloutsos. Edge Weight Prediction in Weighted Signed Networks. IEEE International Conference on Data Mining (ICDM), 2016. http://cs.stanford.edu/~srijan/pubs/wsn-icdm16.pdf The following BibTeX citation can be used: @inproceedings{kumar2016edge, title={Edge weight prediction in weighted signed networks}, author={Kumar, Srijan and Spezzano, Francesca and Subrahmanian, VS and Faloutsos, Christos}, booktitle={Data Mining (ICDM), 2016 IEEE 16th Intl. Conf. on}, pages={221--230}, year={2016}, organization={IEEE} } The project webpage for this paper, along with its code to calculate two signed network metrics---fairness and goodness---is available at http://cs.umd.edu/~srijan/wsn/ Files File Description soc-sign-bitcoinalpha.csv.gz Weighted Signed Directed Bitcoin Alpha web of trust network Data format Each line has one rating with the following format: SOURCE, TARGET, RATING, TIME where SOURCE: node id of source, i.e., rater TARGET: node id of target, i.e., ratee RATING: the source's rating for the target, ranging from -10 to +10 in steps of 1 TIME: the time of the rating, measured as seconds since Epoch. --------------------------------------------------------------------------- Notes on inclusion into the SuiteSparse Matrix Collection, July 2018: --------------------------------------------------------------------------- The SNAP data set is 1-based, with nodes numbered 1 to 7,604. In the SuiteSparse Matrix Collection, Problem.A is the directed rating graph, a matrix of size n-by-n with n=3,783, which is the number of unique node id's appearing in the SOURCE or TARGET of any edge. Problem.aux.nodeid is an array of size 3783 that gives the node id's corresponding to each row and column of the matrix. nodeid(i)=id if the id in the temporal edge appears as the ith row and column of the A matrix. A(i,j) is the rating that member nodeid(i) gave member nodeid(j). Since there are no duplicate ratings, the Problem.A matrix can hold all the edges without losing any information. The ratings are in the range -10 to 10, but are never zero, so the MATLAB Problem.A sparse matrix contains all the edges. The timestamps appear in the Problem.aux.Time matrix. It has the same nonzero pattern as Problem.A. |