Seeing the Forest through the Trees: Adaptive Local Exploration of Large Graphs
This addresses the challenge of meaningless visualizations in large graph exploration for users in data analysis, though it is incremental as it builds on existing local exploration approaches.
The paper tackles the problem of visualizing large graphs by proposing an adaptive local exploration method that helps users explore million-node graphs by showing only the most subjectively interesting neighbors, resulting in rankings that match user interests and linear scalability for very large graphs.
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.