Link analysis is a method used for clustering and classification that identifies relationships between data points through their connections and similarities, commonly applied in areas like network analysis and fraud detection. It employs techniques such as graph-based clustering, link-based similarity measures, and community detection algorithms to analyze and classify data. In clustering and classification, it assists in grouping or labeling data points by leveraging their network relationships, utilizing approaches like relational classification and graph neural networks.

1. Link analysis

2. Link analysis
 Link analysis is a technique used in clustering and
classification to identify relationships between data
points based on connections, associations, or similarities.
It is commonly applied in network analysis, fraud
detection, recommendation systems, and
bioinformatics.
 Graph is widely used in modelling advanced structures and
patterns.

3. Graph-Based Clustering
 Nodes represent entities, and edges represent relationships. Clusters are
formed by finding densely connected subgraphs.
 Link-Based Similarity Measures: Methods like SimRank or PageRank assess
similarity between objects based on their shared connections.
 Community Detection: Algorithms like Louvain method, Girvan-Newman,
and Label Propagation identify naturally occurring communities within a
network.

4. Link Analysis in Classification
 In classification, link analysis helps assign labels to data points based on their
relationships with labeled examples. Some methods include:
 Relational Classification :Uses network structure to infer labels (e.g., if many
friends of a person have a disease, they might be at higher risk).
 Semi-Supervised Learning: A few labeled nodes help classify the entire
network using algorithms like Label Propagation.
 Graph Neural Networks (GNNs): Deep learning models that classify nodes by
aggregating information from their neighbors.

5. Link Analysis in Clustering
 In clustering, link analysis helps group similar data points based on their
relationships or structural connections. Some common approaches include: