Two common machine learning tasks are data classification and data clustering. Among the techniques to solve them, graph-based (or complex-network-based) ones show interesting properties. Such methods usually have two steps:

  1. Graph formation: a graph representation of the dataset is constructed following some specific rules.
  2. Optimization: a objective function (a loss function and a regularizer) is associated with the graph structure. The optimal solution also solves the underlying machine-learning task.

Particularly, clustering tasks can be solved using community detection methods – for example, using modularity optimization. Similarly, graph-based semi-supervised learning solves classification problems in a transductive manner.

Among the advantages of such methods, we highlight:

  • Versatility. Several methods can be employed to construct the graph that represents the dataset, including techniques to deal with tabular data, images, time series, etc. As a result, they work well in many different contexts.
  • Robust even with few data samples. They don’t require large datasets to achieve good results. In the classification scenario, few labeled samples are required.
  • Explicit and interpretable. The objective function is known, so it is usually easy to understand the results.

However, they exhibit a major drawback: solving the optimization problem is usually unfeasible for large datasets. The obvious work-around is using heuristics to find suboptimal solutions.

Early heuristic optimization methods, such as genetic algorithms (GA) and ant colony optimization (ACO), exploit nature- and bio-inspired processes that minimize a cost inherently. However, they rely on ad-hoc computational models for the actions and interactions of autonomous agents with the intent to assess their effects on the system as a whole. As a result, they show strong stochastic behavior leading to a series of downsides, such as instability and low comprehension of the global dynamics.

In this specific context (graph-based methods), recent advances in Network Science and Complex Systems gave rise to interesting heuristics based on collective dynamics to solve machine learning problems.

Consult Verri’s thesis for a more detailed motivation of using collective dynamics as heuristics to solve these problems.

In the next section, we expose collective dynamics to solve machine learning tasks.