# Extract and Visualize Google Scholar Collaboration Networks

**scholarnetwork** is an R package that provides functions to extracts publication information from Google Scholar, create network of collaborators based on co-authored projects, and visualize these networks using a force-directed layout algorithm.

## Installation ##

An initial release of this package is available in this repository (eventually maybe also on CRAN), and can be installed directly using Hadley Wickham's [devtools](http://cran.r-project.org/web/packages/devtools/index.html) package:

```
if(!require("devtools")) install.packages("devtools")
library("devtools")
install_github("pablobarbera/scholarnetwork")
```

## Examples ##

For now, the package consists of two functions, `extractNetwork` and `plotNetwork`, which correspond to the data collection and data visualization steps.

`extractNetwork` wraps the `get_publications` function from the scholar package, which extracts the list of publications on a Google Scholar profile, cleans it, and then parses the results into a format that is more suitable for network analysis: 

- a data frame of __weighted edges__, where each edge is a collaboration in a publication, and the weight is one divided by number of co-authors; and 

- a data frame with __node-level information__, which includes the group resulting from running a walktrap community detection algorithm. 

```r
d <- extractNetwork(id="jGLKJUoAAAAJ", n=500)
str(d)
```
```
List of 2
 $ nodes:'data.frame':	40 obs. of  3 variables:
  ..$ label : chr [1:40] "A Boydstun" "A Valeriani" "A Venetz" "C Roca Cuberes" ...
  ..$ degree: num [1:40] 0.75 1.69 0.75 0.667 1.69 ...
  ..$ group : num [1:40] 11 7 10 8 7 1 3 4 5 13 ...
 $ edges:'data.frame':	106 obs. of  3 variables:
  ..$ node1 : chr [1:106] "P Barberá" "C Vaccari" "K Ackermann" "P Barberá" ...
  ..$ node2 : chr [1:106] "A Boydstun" "A Valeriani" "A Venetz" "A Venetz" ...
  ..$ weight: num [1:106] 0.25 0.31 0.25 0.25 0.25 ...
```

`plotNetwork` takes the lists of nodes and edges returned by `extractNetwork` and visualizes it using `networkD3`. The output of this function is an html file with the network visualization. Note that this function will also work with any other set of edge and node lists.

```r
plotNetwork(d$nodes, d$edges, file="network.html")
```

The output of this function is an interactive visualization of the network. The figure below shows a screenshot of this visualization. Click [__here__](http://pablobarbera.com/static/network.html) for the D3 visualization.

<center><img src="img/interactive-network.png" style="width: 550px;"/></center>


## Additional examples ##

The output of the `extractNetwork` function can also be used to generate a static version of this visualization with e.g. `ggplot2`:

```r
library(ggplot2)
library(igraph)
# cleaning network data
network <- graph_from_data_frame(d$edges, directed=FALSE)
set.seed(123)
l <- layout.fruchterman.reingold(network, niter=1500) # layout
fc <- walktrap.community(network) # community detection

# node locations
nodes <- data.frame(l); names(nodes) <- c("x", "y")
nodes$cluster <- factor(fc$membership)
nodes$label <- fc$names
nodes$degree <- degree(network)

# edge locations
edgelist <- get.edgelist(network, names=FALSE)
edges <- data.frame(nodes[edgelist[,1],c("x", "y")], nodes[edgelist[,2],c("x", "y")])
names(edges) <- c("x1", "y1", "x2", "y2")

# and now visualizing it...
p <- ggplot(nodes, aes(x=x, y=y, color=cluster, label=label, size=degree))
pq <- p + geom_text(color="black", aes(label=label, size=degree),
                    show_guide=FALSE) +
  # nodes
  geom_point(color="grey20", aes(fill=cluster),
             shape=21, show_guide=FALSE, alpha=1/2) +
  # edges
  geom_segment(
    aes(x=x1, y=y1, xend=x2, yend=y2, label=NA),
    data=edges, size=0.25, color="grey20", alpha=1/5) +
  ## note that here I add a border to the points
  scale_fill_discrete(labels=labels) +
  scale_size_continuous(range = c(5, 8)) +
  theme(
    panel.background = element_rect(fill = "white"),
    plot.background = element_rect(fill="white"),
    axis.line = element_blank(), axis.text = element_blank(),
    axis.ticks = element_blank(),
    axis.title = element_blank(), panel.border = element_blank(),
    panel.grid.major = element_blank(),
    panel.grid.minor = element_blank(),
    legend.background = element_rect(colour = F, fill = "black"),
    legend.key = element_rect(fill = "black", colour = F),
    legend.title = element_text(color="white"),
    legend.text = element_text(color="white")
  ) +
  ## changing size of points in legend
  guides(fill = guide_legend(override.aes = list(size=5)))

pq
```

<center><img src="img/network.png" style="width: 550px;"/></center>

However, it is difficult to make sure labels do not overlap. A probably better option is to export the network data to a format that Gephi can read, and then edit it manually in Gephi, as shown below.

```r
df <- data.frame(Source = d$edges$node1, Target = d$edges$node2)
write.csv(df, file="edgelist-gephi.csv", row.names=FALSE)
```

<center><img src="img/collaborator-network.png" style="width: 550px;"/></center>