Introduction to the package

• Network thresholds (Valente, 1995; 1996), \(\tau\), are defined as the required proportion or number of neighbors that leads you to adopt a particular behavior (innovation), \(a=1\). In (very) general terms

\[ a_i = \left\{\begin{array}{ll} 1 &\mbox{if } \tau_i\leq E_i \\ 0 & \mbox{Otherwise} \end{array}\right. \qquad E_i \equiv \frac{\sum_{j\neq i}\mathbf{X}_{ij}a_j}{\sum_{j\neq i}\mathbf{X}_{ij}} \]

Where \(E_i\) is i’s exposure to the innovation and \(\mathbf{X}\) is the adjacency matrix (the network).

• This can be generalized and extended to include covariates and other network weighting schemes (that’s what netdiffuseR is all about).

netdiffuseR

• netdiffuseR is an R package that:

  • Allow to generalize the network threshold concept to include covariates and other network weighting schemes.

  • Is designed for Visualizing, Analyzing and Simulating network diffusion data (in general).

  • Depends on some pretty popular packages:

    • RcppArmadillo: So it’s fast,
    • Matrix: So it’s big,
    • statnet and igraph: So it’s not from scratch

  • Can handle big graphs, e.g., an adjacency matrix with more than 4 billion elements (PR for RcppArmadillo).

  • Already on CRAN with +48,000 downloads since its first version, Feb 2016.

• Datasets

  • netdiffuseR has the three classic Diffusion Network Datasets, both as surveys:

    • medInnovations Doctors and the innovation of Tetracycline (1955),
    • brfarmers Brazilian farmers and the innovation of Hybrid Corn Seed (1966),
    • kfamily Korean women and Family Planning methods (1973),

    and as diffnet objects:

    • medInnovationsDiffNet Doctors and the innovation of Tetracycline (1955),
    • brfarmersDiffNet Brazilian farmers and the innovation of Hybrid Corn Seed (1966),
    • kfamilyDiffNet Korean women and Family Planning methods (1973)

    brfarmersDiffNet

    # Dynamic network of class -diffnet-
    #  Name               : Brazilian Farmers
    #  Behavior           : Adoption of Hybrid Corn Seeds
    #  # of nodes         : 692 (1001, 1002, 1004, 1005, 1007, 1009, 1010, 1020, ...)
    #  # of time periods  : 21 (1946 - 1966)
    #  Type               : directed
    #  Num of behaviors   : 1
    #  Final prevalence   : 1.00
    #  Static attributes  : village, idold, age, liveout, visits, contact, coo... (146)
    #  Dynamic attributes : -

    medInnovationsDiffNet

    # Dynamic network of class -diffnet-
    #  Name               : Medical Innovation
    #  Behavior           : Adoption of Tetracycline
    #  # of nodes         : 125 (1001, 1002, 1003, 1004, 1005, 1006, 1007, 1008, ...)
    #  # of time periods  : 18 (1 - 18)
    #  Type               : directed
    #  Num of behaviors   : 1
    #  Final prevalence   : 1.00
    #  Static attributes  : city, detail, meet, coll, attend, proage, length, ... (58)
    #  Dynamic attributes : -

    kfamilyDiffNet

    # Dynamic network of class -diffnet-
    #  Name               : Korean Family Planning
    #  Behavior           : Family Planning Methods
    #  # of nodes         : 1047 (10002, 10003, 10005, 10007, 10010, 10011, 10012, 10014, ...)
    #  # of time periods  : 11 (1 - 11)
    #  Type               : directed
    #  Num of behaviors   : 1
    #  Final prevalence   : 1.00
    #  Static attributes  : village, recno1, studno1, area1, id1, nmage1, nmag... (430)
    #  Dynamic attributes : -