Introduction to Social Graph Analysis

Social graph analysis is a field of study that deals with the representation and analysis of relationships between individuals or entities in a social network. The data used for this analysis can be in various formats, including edgelist files in Pajek format, CSV files, and other data structures. In this article, we will discuss how to analyze a large social graph with 100 million nodes and 60 GB of memory limitations.

Understanding the Problem

The user’s question highlights two main challenges: (1) handling large amounts of data (100 million nodes) and (2) dealing with memory constraints (6 GB limit). The user is considering using R and the igraph package for analysis, but this approach seems to be insufficient due to memory limitations.

Alternative Approaches

One alternative approach that the user suggests is using the bigtablulate package in R. This package allows for file-backed objects, which can help alleviate memory constraints by storing data on disk instead of in RAM. Another approach mentioned is parallelizing degree computations using the foreach package.

Bigtabluate Package Overview

The bigtablulate package provides a convenient way to work with large datasets in R. It allows users to store their objects on disk and retrieve them as needed, which can help reduce memory usage. This package also provides functions for parallelizing computations, such as the foreach package.

Key Features of Bigtablulate Package

• File-backed objects: Store data on disk instead of in RAM.
• Parallelization: Allows users to take advantage of multiple CPU cores to speed up computations.
• Convenience functions: Provides a range of functions for common tasks, including data loading and manipulation.

Example Use Case: Analyzing an Edgelist

To demonstrate the use of bigtablulate package, let’s create an example with an edgelist involving 1 million edges among 1 million nodes. We will then concatenate this file 10 times to make the example a bit bigger.

Code

set.seed(1)
N <- 1e6
M <- 1e6
edgelist <- cbind(sample(1:N,M,replace=TRUE),
                  sample(1:N,M,replace=TRUE))
colnames(edgelist) <- c("sender","receiver")
write.table(edgelist,file="edgelist-small.csv",sep=",",
            row.names=FALSE,col.names=FALSE)
system("for i in $(seq 1 10) do 
do 
  cat edgelist-small.csv >> edgelist.csv 
done")
library(bigtabulate)
x <- read.big.matrix("edgelist.csv", header = FALSE, type = "integer",
                     backingfile = "edgelist.bin", descriptor = "edgelist.desc")
nrow(x)  # 1e7 as expected
outdegree <- bigtable(x,1)
head(outdegree)

Explanation

The code creates a sample edgelist with 1 million edges among 1 million nodes. It then concatenates this file 10 times to make the example bigger.

We load the bigtablulate package and read in the text file with our edgelist using read.big.matrix(). This function creates a file-backed object in R, which can help alleviate memory constraints by storing data on disk instead of in RAM.

Computing Outdegrees

To compute outdegrees, we use the bigtable() function on the first column. The resulting table is stored in the outdegree variable.

Code

outdegree <- bigtable(x,1)
head(outdegree)

Explanation

This code computes the outdegrees of each node in the graph by applying the bigtable() function to the first column of the x matrix. The resulting table is stored in the outdegree variable.

Sanity Check

To ensure that the table worked as expected, we perform a sanity check using two methods: (1) comparing the outdegree value with the manual count of outgoing edges for each node, and (2) verifying that the table contains the correct data for the first node.

Code

j <- as.numeric(names(outdegree[1]))  # get name of first node
all.equal(as.numeric(outdegree[1]), sum(x[,1]==j))  # outdegree's answer

Explanation

This code performs two sanity checks: (1) it compares the outdegree value with the manual count of outgoing edges for each node, and (2) it verifies that the table contains the correct data for the first node.

Conclusion

Social graph analysis is a complex task that requires careful consideration of memory constraints. The bigtablulate package provides a convenient way to work with large datasets in R by storing objects on disk instead of in RAM. By parallelizing computations using foreach and leveraging file-backed objects, users can efficiently analyze large social graphs even when faced with limited memory.

Best Practices for Social Graph Analysis

• Choose an efficient data structure: Edgelist files are often used for social graph analysis due to their simplicity and ease of use.
• Leverage parallelization: Using foreach or other parallelization tools can significantly speed up computations on large datasets.
• Optimize memory usage: File-backed objects like those provided by bigtablulate package can help alleviate memory constraints.

Resources

• Bigtabluate Package Documentation: https://github.com/hadley/bigtable
• Foreach Package Documentation: http://stackoverflow.com/questions/14050900/how-to-use-parallel-processing-in-r-with-the-foreach-package
• R and Graph Analysis: https://cran.r-project.org/web/packages/igraph/index.html

Last modified on 2024-01-08