A High-Scalability Graph Modification System for Large-Scale Networks

Modifying network results is the most intuitive way to inject domain knowledge into network detection algorithms to improve their performance. While advances in computation scalability have made detecting large-scale networks possible, the human ability to modify such networks has not scaled accordingly, resulting in a huge 'interaction gap'. Most existing works only support navigating and modifying edges one by one in a graph visualization, which causes a significant interaction burden when faced with large-scale networks. In this work, we propose a novel graph pattern mining algorithm based on the minimum description length (MDL) principle to partition and summarize multi-feature and isomorphic sub-graph matches. The mined sub-graph patterns can be utilized as mediums for modifying large-scale networks. Combining two traditional approaches, we introduce a new coarse-middle-fine graph modification paradigm (i.e. query graph-based modification ->$\rightarrow$ sub-graph pattern-based modification ->$\rightarrow$ raw edge-based modification). We further present a graph modification system that supports the graph modification paradigm for improving the scalability of modifying detected large-scale networks. We evaluate the performance of our graph pattern mining algorithm through an experimental study, demonstrate the usefulness of our system through a case study, and illustrate the efficiency of our graph modification paradigm through a user study. While advances in computation scalability have made detecting large-scale networks possible, the human ability to modify such networks has not scaled accordingly. We present a high-scalability graph modification system to bridge the interaction gap between detecting and visualizing large-scale networks and humans' ability to modify these networks. image