Active Search and Bandits on Graphs using Sigma-Optimality

Many modern information access problems involve highly complex patterns that cannot be handled by traditional keyword based search. Active Search is an emerging paradigm that helps users quickly find relevant information by efficiently collecting and learning from user feedback. We consider active search on graphs, where the nodes represent the set of instances users want to search over and the edges encode pairwise similarity among the instances. Existing active search algorithms are either short of theoretical guarantees or inadequate for graph data. Motivated by recent advances in active learning on graphs, namely the Σ-optimality selection criterion, we propose new active search algorithms suitable for graphs with theoretical guarantees and demonstrate their effectiveness on several real-world datasets. We relate our active search setting to multi-armed bandits whose rewards are binary values indicating search hits or misses and arms cannot be pulled more than once. We also discussed theoretical guarantees for applying Σ-optimality as the exploration term for bandits on graphs.