Seeing the Forest through the Trees: Adaptive Local Exploration of Large Graphs

TL;DR

FACETS is a scalable visualization method that enables users to explore large graphs locally by highlighting interesting neighborhoods based on surprise and fit, improving interpretability and user engagement.

Contribution

The paper introduces FACETS, a novel scalable approach for local graph exploration that measures neighborhood interestingness using surprise and fit, tailored for large graphs.

Findings

FACETS effectively ranks nodes matching user interests.

The method scales linearly to very large graphs.

Empirical results demonstrate practical usefulness.

Abstract

Visualization is a powerful paradigm for exploratory data analysis. Visualizing large graphs, however, often results in a meaningless hairball. In this paper, we propose a different approach that helps the user adaptively explore large million-node graphs from a local perspective. For nodes that the user investigates, we propose to only show the neighbors with the most subjectively interesting neighborhoods. We contribute novel ideas to measure this interestingness in terms of how surprising a neighborhood is given the background distribution, as well as how well it fits the nodes the user chose to explore. We introduce FACETS, a fast and scalable method for visually exploring large graphs. By implementing our above ideas, it allows users to look into the forest through its trees. Empirical evaluation shows that our method works very well in practice, providing rankings of nodes that match interests of users. Moreover, as it scales linearly, FACETS is suited for the exploration of very large graphs.