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RCAnalyzer: Visual Analytics of Rare Categories in Dynamic Networks

RCAnalyzer: Visual Analytics of Rare Categories in Dynamic Networks

Abstract

A dynamic network refers to a graph structure whose nodes and/or links will dynamically change over time. Existing visualization and analysis techniques mainly focus on summarizing and revealing the primary evolution patterns of the network structure. Little work focuses on detecting anomalous changing patterns in a dynamic network, the rare occurrence of which could damage the development of the entire structure. In this paper, we introduce the first visual analysis system RCAnalyzer designed for detecting rare changes of sub-structures in a dynamic network. The proposed system employs a rare category detection algorithm to identify anomalous changing structures and visualize them in context to help oracles examine the analysis results and label the data. In particular, a novel visualization is introduced, which represents the snapshots of a dynamic network in a series of connected triangular matrices. Hierarchical clustering and optimal tree cut are performed on each matrix to illustrate the detected rare change of nodes and links in the context of their surrounding structures. We evaluate our technique via a case study and a user study. The evaluation results verified the effectiveness of our system.

Publication
Frontiers of Information Technology & Electronic Engineering