node2vec
1. Understanding the Input Data
Text Units:
The text units are chunks of text data, each with a unique ID (chunk_id) and associated with a document ID (document_ids)
Each chunk of text is processed to extract entities, which are likely phrases, words, or other meaningful segments
2. Node2Vec and Graph Representation
Graph Construction
From these text units, entities might be extracted and represented as nodes in a graph, while relationships between these entities (e.g., co-occurrence, similarity, etc.) might be represented as edges
For example, if two entities frequently co-occur within the same text chunk, they might be connected by an edge in the graph
node2vec is a machine learning algorithm that generates vector representations (embeddings) for nodes in a graph
It does this by simulating random walks on the graph and then applying a skip-gram model (similar to the word2vec algorithm) to learn the embeddings
The embeddings capture the structural relationships in the graph, meaning that nodes that are closely related or share similar neighborhoods in the graph will have similar embeddings.
3. Application of Node2Vec to This Data
GraphML
The results you shared include GraphML data, which is a structured representation of a graph in XML format
This suggests that the text data was transformed into a graph (likely with entities as nodes and their relationships as edges) and then saved in GraphML format
Clustering
The node2vec embeddings could be used as part of a clustering algorithm to group similar entities together based on their embeddings
The "clustered graph" output likely represents the graph after the nodes have been clustered into communities or groups