R: Network Objects

network {network} | R Documentation

## Network Objects

### Description

Construct, coerce to, test for and print `network` objects.

### Usage

    is.network(x)

    as.network(x, ...)

    network(
      x,
      vertex.attr = NULL,
      vertex.attrnames = NULL,
      directed = TRUE,
      hyper = FALSE,
      loops = FALSE,
      multiple = FALSE,
      bipartite = FALSE,
      ...
    )

    network.copy(x)

    ## S3 method for class 'data.frame'
    as.network(
      x,
      directed = TRUE,
      vertices = NULL,
      hyper = FALSE,
      loops = FALSE,
      multiple = FALSE,
      bipartite = FALSE,
      bipartite_col = "is_actor",
      ...
    )

    ## S3 method for class 'network'
    print(
      x,
      matrix.type = which.matrix.type(x),
      mixingmatrices = FALSE,
      na.omit = TRUE,
      print.adj = FALSE,
      ...
    )

    ## S3 method for class 'network'
    summary(object, na.omit = TRUE, mixingmatrices = FALSE, print.adj = TRUE, ...)

### Arguments

`x` | for `network`, a matrix giving the network structure in adjacency, incidence, or edgelist form; otherwise, an object of class `network`.
`...` | additional arguments.
`vertex.attr` | optionally, a list containing vertex attributes.
`vertex.attrnames` | optionally, a list containing vertex attribute names.
`directed` | logical; should edges be interpreted as directed?
`hyper` | logical; are hyperedges allowed?
`loops` | logical; should loops be allowed?
`multiple` | logical; are multiplex edges allowed?
`bipartite` | count; should the network be interpreted as bipartite? If present (i.e., non-NULL, non-FALSE) it is the count of the number of actors in the bipartite network. In this case, the number of nodes is equal to the number of actors plus the number of events (with all actors preceeding all events). The edges are then interpreted as nondirected. Values of bipartite==0 are permited, indicating a bipartite network with zero-sized first partition.
`vertices` | If `x` is a `data.frame`, `vertices` is an optional `data.frame` containing the vertex attributes. The first column is assigned to the `"vertex.names"` and additional columns are used to set vertex attributes using their column names. If `bipartite` is `TRUE`, a `logical` column named `"is_actor"` (or the name of a column specified using the `bipartite_col` parameter) can be provided indicating which vertices should be considered as actors. If not provided, vertices referenced in the first column of `x` are assumed to be the network's actors. If your network has isolates (i.e. there are vertices referenced in `vertices` that are not referenced in `x`), the `"is_actor"` column is required.
`bipartite_col` | `character(1L)`, default: `"is_actor"`. The name of the `logical` column indicating which vertices should be considered as actors in bipartite networks.
`matrix.type` | one of `"adjacency"`, `"edgelist"`, `"incidence"`. See `edgeset.constructors` for details and optional additional arguments
`mixingmatrices` | logical; print the mixing matrices for the discrete attributes?
`na.omit` | logical; omit summarization of missing attributes in `network`?
`print.adj` | logical; print the network adjacency structure?
`object` | an object of class `network`.

### Details

`network` constructs a `network` class object from a matrix representation. If the `matrix.type` parameter is not specified, it will make a guess as to the intended `edgeset.constructors` function to call based on the format of these input matrices. If the class of `x` is not a matrix, network construction can be dispatched to other methods. For example, If the `ergm` package is loaded, `network()` can function as a shorthand for `as.network.numeric` with `x` as an integer specifying the number of nodes to be created in the random graph.

If the `ergm` package is loaded, `network` can function as a shorthand for `as.network.numeric` if `x` is an integer specifying the number of nodes. See the help page for `as.network.numeric` in `ergm` package for details.

`network.copy` creates a new `network` object which duplicates its supplied argument. (Direct assignment with `<-` should be used rather than `network.copy` in most cases.)

`as.network` tries to coerce its argument to a network, using the `as.network.matrix` functions if `x` is a matrix. (If the argument is already a network object, it is returned as-is and all other arguments are ignored.)

`is.network` tests whether its argument is a network (in the sense that it has class `network`).

`print.network` prints a network object in one of several possible formats. It also prints the list of global attributes of the network.

`summary.network` provides similar information.

### Value

`network`, `as.network`, and `print.network` all return a network class object; `is.network` returns TRUE or FALSE.

### Note

Between versions 0.5 and 1.2, direct assignment of a network object created a pointer to the original object, rather than a copy. As of version 1.2, direct assignment behaves in the same manner as `network.copy`. Direct use of the latter is thus superfluous in most situations, and is discouraged.

Many of the network package functions modify their network object arguments in-place. For example, `set.network.attribute(net,"myVal",5)` will have the same effect as `net<-set.network.attribute(net,"myVal",5)`. Unfortunately, the current implementation of in-place assignment breaks when the network argument is an element of a list or a named part of another object. So `set.network.attribute(myListOfNetworks[[1]],"myVal",5)` will silently fail to modify its network argument, likely leading to incorrect output.

### Author(s)

Carter T. Butts buttsc@uci.edu and David Hunter dhunter@stat.psu.edu

### References

Butts, C. T. (2008). “network: a Package for Managing Relational Data in R.” Journal of Statistical Software, 24(2). https://www.jstatsoft.org/v24/i02/

### See Also

`network.initialize`, `attribute.methods`, `as.network.matrix`, `as.matrix.network`, `deletion.methods`, `edgeset.constructors`, `network.indicators`, `plot.network`

### Examples

    m <- matrix(rbinom(25,1,.4),5,5)
    diag(m) <- 0
    g <- network(m, directed=FALSE)
    summary(g)

    h <- network.copy(g)       #Note: same as h<-g
    summary(h)

    # networks from data frames ===========================================================
    #* simple networks ====================================================================
    simple_edge_df <- data.frame(
      from = c("b", "c", "c", "d", "a"),
      to = c("a", "b", "a", "a", "b"),
      weight = c(1, 1, 2, 2, 3),
      stringsAsFactors = FALSE
    )
    simple_edge_df

    as.network(simple_edge_df)

    # simple networks with vertices =======================================================
    simple_vertex_df <- data.frame(
      name = letters[1:5],
      residence = c("urban", "rural", "suburban", "suburban", "rural"),
      stringsAsFactors = FALSE
    )
    simple_vertex_df

    as.network(simple_edge_df, vertices = simple_vertex_df)

    as.network(simple_edge_df,
      directed = FALSE, vertices = simple_vertex_df,
      multiple = TRUE
    )

    #* splitting multiplex data frames into multiple networks =============================
    simple_edge_df$relationship <- c(rep("friends", 3), rep("colleagues", 2))
    simple_edge_df

    lapply(split(simple_edge_df, f = simple_edge_df$relationship),
      as.network,
      vertices = simple_vertex_df
    )

    #* bipartite networks without isolates ================================================
    bip_edge_df <- data.frame(
      actor = c("a", "a", "b", "b", "c", "d", "d", "e"),
      event = c("e1", "e2", "e1", "e3", "e3", "e2", "e3", "e1"),
      actor_enjoyed_event = rep(c(TRUE, FALSE), 4),
      stringsAsFactors = FALSE
    )
    bip_edge_df

    bip_node_df <- data.frame(
      node_id = c("a", "e1", "b", "e2", "c", "e3", "d", "e"),
      node_type = c(
        "person", "event", "person", "event", "person",
        "event", "person", "person"
      ),
      color = c(
        "red", "blue", "red", "blue", "red", "blue",
        "red", "red"
      ),
      stringsAsFactors = FALSE
    )
    bip_node_df

    as.network(bip_edge_df, directed = FALSE, bipartite = TRUE)
    as.network(bip_edge_df, directed = FALSE, vertices = bip_node_df, bipartite = TRUE)

    #* bipartite networks with isolates ===================================================
    bip_nodes_with_isolates <- rbind(
      bip_node_df,
      data.frame(
        node_id = c("f", "e4"),
        node_type = c("person", "event"),
        color = c("red", "blue"),
        stringsAsFactors = FALSE
      )
    )
    # indicate which vertices are actors via a column named `"is_actor"`
    bip_nodes_with_isolates$is_actor <- bip_nodes_with_isolates$node_type == "person"
    bip_nodes_with_isolates

    as.network(bip_edge_df,
      directed = FALSE, vertices = bip_nodes_with_isolates,
      bipartite = TRUE
    )

    #* hyper networks from data frames ====================================================
    hyper_edge_df <- data.frame(
      from = c("a/b", "b/c", "c/d/e", "d/e"),
      to = c("c/d", "a/b/e/d", "a/b", "d/e"),
      time = 1:4,
      stringsAsFactors = FALSE
    )
    tibble::as_tibble(hyper_edge_df)

    # split "from" and "to" at `"/"`, coercing them to list columns
    hyper_edge_df$from <- strsplit(hyper_edge_df$from, split = "/")
    hyper_edge_df$to <- strsplit(hyper_edge_df$to, split = "/")
    tibble::as_tibble(hyper_edge_df)

    as.network(hyper_edge_df,
      directed = FALSE, vertices = simple_vertex_df,
      hyper = TRUE, loops = TRUE
    )

    # convert network objects back to data frames =========================================
    simple_g <- as.network(simple_edge_df, vertices = simple_vertex_df)
    as.data.frame(simple_g)
    as.data.frame(simple_g, unit = "vertices")

    bip_g <- as.network(bip_edge_df,
      directed = FALSE, vertices = bip_node_df,
      bipartite = TRUE
    )
    as.data.frame(bip_g)
    as.data.frame(bip_g, unit = "vertices")

    hyper_g <- as.network(hyper_edge_df,
      directed = FALSE, vertices = simple_vertex_df,
      hyper = TRUE, loops = TRUE
    )
    as.data.frame(hyper_g)
    as.data.frame(hyper_g, unit = "vertices")

[Package network version 1.17.2 Index]