Chapter 11 Dynamic Edges

So far we have been drawing static graphs, in this chapter we look at dynamic ones, namely temporal. However, being an introduction we’ll start with dynamic edges only: we’ll plot the nodes and have the edges appear dynamically .We’ll tackle the fully temporal network further down the book.

11.1 Rationale

We’ve been visualising Twitter interactions in a static manner, but they are dynamic when you think of it. Twitter conversations happen over time, thus far, we’ve just been drawing all encompassing snapshots. So let’s take into account the time factor to make a where the edges appear at different time steps.

11.2 Collect

We’ll collect some tweets again, we’ll use retweets this time, so we build the corresponding search.

# TK <- readRDS(file = "token.rds")
tweets <- search_tweets("#rstats filter:retweets", n = 400, token = TK, include_rts = TRUE)
## Searching for tweets...
## Finished collecting tweets!

11.3 Build

Now onto building the graph.

net <- tweets %>% 
  gt_edges(screen_name, mentions_screen_name, created_at) %>% 
  gt_nodes() %>% 
  gt_dyn() %>% 
  gt_collect()

Quite a few things differ from previous graphs we have built.

  1. We pass created_at in gt_edges. This in effect adds the created_at column to our edges, so that we know the created time of post in which the edge appears.
  2. We use gt_dyn which stands for dynamic, to essentially compute the time at which edges and nodes should appear on the graph.
head(net$edges)
source target created_at n end
_100daysofcode nitish_sharma23 2019-03-09 17:11:37 1 2019-03-09 18:39:21
_reactdev gp_pulipaka 2019-03-09 15:22:39 1 2019-03-09 18:39:21
_reactdev gp_pulipaka 2019-03-09 15:50:59 2 2019-03-09 18:39:21
_reactdev gp_pulipaka 2019-03-09 16:02:39 1 2019-03-09 18:39:21
_reactdev neptanum 2019-03-09 17:16:00 1 2019-03-09 18:39:21
_reactdev nitish_sharma23 2019-03-09 16:47:40 3 2019-03-09 18:39:21

11.4 Visualise

Now for the visualisation, let’s build it step by step; first we prep the data as we did before: renaming a few columns but also running a few unfamiliar computations.

To explain how we build the visualisation, we first need to tell you how the edges will dynamically appear on the graph. The way this works in sigmajs is by specifying the delay in milliseconds before each respective edge should be added. Therefore, we need to transform the date to milliseconds and rescale them to be within a reasonable range: we don’t want the edges to actually take 15 days to appear on the graph.

  1. We change the date time column (POSIXct actually) to a numeric, which gives the number of milliseconds since 1970.
  2. We rescale between 0 and 1 then multiply by 10,000 (milliseconds) so that the edges are added over 10 seconds.
library(dplyr)

c(edges, nodes) %<-% net # unpack

nodes <- nodes2sg(nodes)

edges <- edges %>% 
  mutate(
    id = 1:n(),
    created_at = as.numeric(created_at),
    created_at = (created_at - min(created_at)) / (max(created_at) - min(created_at)),
    created_at = created_at * 10000
  ) %>% 
  select(id, source, target, created_at)

Let’s inspect what we obtain.

head(net$edges)
id source target created_at
1 _100daysofcode nitish_sharma23 5962.8806
2 _reactdev gp_pulipaka 948.6924
3 _reactdev gp_pulipaka 2252.4733
4 _reactdev gp_pulipaka 2789.3243
5 _reactdev neptanum 6164.5832
6 _reactdev nitish_sharma23 4860.8022

We see that the column created_at has changed from Date time (POSIXct) to a numeric. As mentioned previously, we rescaled it to be between 0 and 10,000 milliseconds, let’s see if that is correct.

range(edges$created_at)
## [1]     0 10000

So for instance the edge at row 25, a tweet where @aihub2 tags @neptanum will appear after 6100 milliseconds.

edges[25,]
## # A tibble: 1 x 4
##      id source target   created_at
##   <int> <chr>  <chr>         <dbl>
## 1    25 aihub2 neptanum      6100.

Now, the actual visualisation, as mentioned at the begining to the chapter, we’ll plot the nodes then add edges dynamically. Let’s break it down step by step.

First, we plot the nodes.

sigmajs() %>% 
  sg_nodes(nodes, id, size, label) 

We’ll add the layout as it looks a bit messy with nodes randomly scattered across the canvas. We’ll have to compute the layout differently this time, we cannot simply use sg_layout as it requires both nodes and edges and we only have nodes on the graph (since edges are to be added later on, dynamically); instead we use sg_get_layout.

You cannot use sg_cluster and sg_layout in dynamic graphs as they require both nodes and edges, use the sg_get_* alternatives.

This is something that we had not shared with you earlier on, sg_nodes must have x and y coordinates of each node, however, if missing they are generated randomly by the package. sg_get_layout computes the coordinates of the nodes (x and y) and adds them to our nodes data.frame.

nodes <- sg_get_layout(nodes, edges, layout = igraph::layout_components)
head(nodes)
id label start end type size x y
_100daysofcode _100daysofcode 2019-03-09 17:11:37 2019-03-09 18:39:21 user 1 1.8841751 19.57333
_reactdev _reactdev 2019-03-09 15:22:39 2019-03-09 18:39:21 user 6 -0.6463731 15.53491
serverlessbot serverlessbot 2019-03-09 15:40:45 2019-03-09 18:39:21 user 1 -0.1131114 10.67885
2bftawfik 2bftawfik 2019-03-09 16:35:51 2019-03-09 18:39:21 user 2 -0.0105335 19.03388
65siddy 65siddy 2019-03-09 16:07:20 2019-03-09 18:39:21 user 1 -5.1234614 14.64823
7ed3303103e74ea 7ed3303103e74ea 2019-03-09 15:12:14 2019-03-09 18:39:21 user 1 -0.6099485 11.02594

Now we can simply pass the coordinates x and y to sg_nodes.

sigmajs() %>% 
  sg_nodes(nodes, id, size, label, x, y) 

Let’s beautify the graph a little, this deep black is somewhat unnerving.

sigmajs() %>% 
  sg_nodes(nodes, id, size, label, x, y) %>%
  sg_settings(
    defaultNodeColor = "#127ba3",
    edgeColor = "default",
    defaultEdgeColor = "#d3d3d3",
    minNodeSize = 1,
    maxNodeSize = 4,
    minEdgeSize = 0.3,
    maxEdgeSize = 0.3
  )

Now we have something that looks like a graph, except it’s missing edges. Let’s add them.

We add the edges almost exactly as we did before, we use sg_add_edges instead of sg_edges. Other than the function name, the only difference is that we pass created_at as delay. We also set cumsum to FALSE otherwise the function computes the cumulative sum on the delay, which is, here, our created_at column, and does not require counting the cumulative sum.

sigmajs() %>% 
  sg_nodes(nodes, id, size, label, x, y) %>%
  sg_add_edges(edges, created_at, id, source, target, cumsum = FALSE, refresh = TRUE) %>% 
  sg_settings(
    defaultNodeColor = "#127ba3",
    edgeColor = "default",
    defaultEdgeColor = "#d3d3d3",
    minNodeSize = 1,
    maxNodeSize = 4,
    minEdgeSize = 0.3,
    maxEdgeSize = 0.3
  )

Now the edges appear dynamically. However you probably missed that as the animation is triggered when the page is loaded, the edges appear dynamically as you were reading this page. sigmajs provides an easy workaround: we can add a button for the user to trigger the animation themself.

The button is added with sg_button to which we pass a label (Add edges) and the event (add_edges) the button will trigger. The name of the event corresponds to the function it essentially triggers minus the starting sg_. In our case add_edges triggers sg_add_edges. Many events can be triggered by the button, they are listed on sigmajs official website.

Click the button in the top right corner of the visualisation to add the edges, it’ll take 10 seconds for all of them to be on the graph.

sigmajs() %>% 
  sg_nodes(nodes, id, size, label, x, y) %>%
  sg_add_edges(edges, created_at, id, source, target, cumsum = FALSE, refresh = TRUE) %>% 
  sg_button("add_edges", "Add edges") %>% 
  sg_settings(
    defaultNodeColor = "#127ba3",
    edgeColor = "default",
    defaultEdgeColor = "#d3d3d3",
    minNodeSize = 1,
    maxNodeSize = 4,
    minEdgeSize = 0.3,
    maxEdgeSize = 0.3
  )

A sigmajs button can trigger multiple events.

We can see that some nodes are better at diseminating the message (#rstats), as the message reaches them they trigger numerous re-tweets.