Social network analysis with machine learning

September 20, 2022

Introduction

Social network analysis is an essential technique that helps experts understand how people interact with each other in complex web structures. With machine learning capabilities, social network analysis becomes even more powerful, enabling us to identify actionable insights at scale. The reason why machine learning is such a great match for social network analysis is that it can help us identify sophisticated patterns in large data sets, allowing us to understand the structural components of social networks better. In this blog post, we will discuss exactly how social network analysis can work in conjunction with machine learning algorithms.

Step 1: Preprocessing

The first step in implementing social network analysis is to preprocess your data. There are different ways to store social network data, but the most common technique is to use graph data structures. In a graph, each node represents a person or entity, and the edges show the linkages between those entities. With this data structure, we can then use machine learning algorithms to generate insights based on the network’s topology.

To work with graph data, we must import the relevant libraries such as networkx, Pandas, and Numpy. For instance, ’networkx’ is a Python package that provides tools for working with complex networks. Here is a sample code snippet that creates a graph object using ’networkx':

import networkx as nx
graph = nx.Graph()

Step 2: Exploring the graph structure

Once we have imported our data, we can then start exploring the graph structure, identifying the central nodes, and understanding their properties. So, what are the properties that we should be looking for? These include the degree, clustering coefficient, betweenness centrality, and closeness centrality. The degree of a node refers to the number of edges that it has, while the clustering coefficient is an indication of how closely the node’s neighbors are connected. The betweenness centrality measures how much the node connects different parts of the network, while the closeness centrality measures how close a node is to all the other nodes in the network, taking into account the shortest distances between the nodes.

To calculate these network properties, we can use various algorithms such as the Girvan�Newman algorithm, which is a hierarchical clustering algorithm for graphs. Here is a sample code snippet that calculates the clustering coefficient using the ’networkx’ package:

from networkx.algorithms import cluster
cluster.cn_soundarajan_hopcroft(graph)

Step 3: Supervised machine learning

Once we understand the network’s structure’s properties, we can use them to feed into supervised machine learning algorithms. Supervised machine learning algorithms are designed to predict an outcome, given input features. In social network analysis, the outcome could be a particular person’s behavior, such as their propensity to buy a product, or their likelihood of spreading certain ideas. We can use network properties like degree, clustering coefficient, and centrality measures as inputs to our model.

The most commonly used machine learning techniques for social network analysis are classification algorithms such as logistic regression or decision trees. For example, we can use logistic regression to predict which nodes are more prone to participate in a particular behavior. Logistic regression is a supervised learning algorithm that can predict the probability of a binary outcome based on one or more input features. Here is a sample code snippet that implements logistic regression using the ‘sklearn’ package:

from sklearn.linear_model import LogisticRegression

X = ['degree', 'clustering_coefficient']
Y = ['participation']
log_reg = LogisticRegression()
log_reg.fit(X, Y)
log_reg.predict(X)

Step 4: Unsupervised machine learning

Another approach to social network analysis is to use unsupervised machine learning algorithms. Unsupervised learning algorithms are used when we do not have labeled training data. These algorithms identify patterns in the data, group together similar nodes or entities, and identify subnetworks within the larger network. This approach is useful when we want to identify emergent patterns in the data.

One of the most popular unsupervised learning algorithms for social network analysis is the community detection algorithm. The algorithm identifies clusters of nodes that are more densely linked internally than they are with nodes outside the cluster. Here is a code snippet that implements community detection using the ‘igraph’ package:

from igraph import Graph
g = Graph()
g.add_vertices(10)
g.add_edges([(0, 4), (4, 2), (2, 3), (3, 1), (1, 6), (6, 7), (7, 6)])
g.add_edges([(8, 5), (5, 9), (9, 8)])

g.community_edge_betweenness().as_clustering()

Conclusion

In conclusion, social network analysis is a powerful technique that can provide insights into how people interact with each other online. Combining machine learning algorithms with social networks can lead to better insights into the community structure, outlier detection, and recommendation systems. This post covered some of the core concepts of social network analysis and machine learning, such as preprocessing, graph exploration, supervised and unsupervised machine learning, and provided code snippets to showcase the use of different packages. Finally, for more information about social network analysis and machine learning, readers can refer to resources such as academic papers or online courses.