Machine learning on graphs

Machine learning techniques (such as classification or clustering) can nowadays be applied to nodes, edges, or entire graphs. The concepts remain the same, but we apply the algorithms to graph entities, and therefore, some of the tasks can be framed as a node, link, or subgraph classification. For example, in a network of users on social media, identifying abusive or bot users would be a node classification task. Identifying malicious messages or transactions would be an edge classification problem. Detecting groups of hate speech disseminators would be a graph classification problem.

In graph machine learning, the challenge lies in extracting features from a graph. A possible approach would be using the adjacency matrix and node features as an attribute vector and feeding it to a traditional machine learning algorithm. However, the model produced will not be permutation-invariant, as there is no inherent order within the nodes in a graph; models based...