Cluster analysis on dynamic graphs - Lee, Cecilia Si Si (Nanyang Technological University, 2022)
As the collection of data becomes common with the big data revolution, an increasing number of methods are created for data analysis. One important field of data is graph data. Much research has been done for static graph analysis in recent years. However, graph data today is continuously evolving (not static), especially with the constant expansion of the Internet. For example, social networks are changing every second as users can add or remove social connections. Clustering is often used as a technique to identify the different social clusters. Traditionally, graphs are represented as adjacency lists and adjacency matrices. However, as data size increases, it becomes impractical to store graphs in adjacency lists and matrices. Thus, graphs are represented as embeddings. Embedding is a compressed representation of the graph, and it is a vector-based representation of the graph. Thus, this representation can be used as input for machine learning.