Self-Organizing Map-Based Graph Clustering and Visualization on Streaming Graphs

Abstract

Many real-world networks, such as computer networks, social networks, and the Internet of Things (loT), can be represented by streaming (or dynamic) graphs. Analysis of these streaming graphs serves as the basis for classification, anomaly detection, community detection, clustering, and visual-ization tasks. This paper uses a Self-Organizing Map (SOM), an unsupervised learning model, to cluster and visualize streaming graphs. As a result, a SOM is used to visualize and interpret the anomaly detection technique on high-dimensional graph-structured data. For this, the SOM-based graph clustering and visualization technique is divided into two phases. In the first phase, we use various existing graph sketching techniques like StreamS pot, SpotLight, and SnapSketch to embed streaming graphs into sketched vectors. Later, in the second phase, we pass the sketched vector inputs into a SOM to cluster and visualize the normal and anomalous graph streams to interpret the anomaly detection technique. In addition, the SOM-based visualization also helps to estimate the quality of embedding (or sketching) techniques.