Active search (AS) on graphs focuses on collecting certain labeled nodes (targets) given global knowledge of the network topology and its edge weights under a query budget. However, in most networks, nodes, topology and edge weights are all initially unknown. We introduce selective harvesting, a variant of AS where the next node to be queried must be chosen among the neighbors of the current queried node set; the available training data for deciding which node to query is restricted to the subgraph induced by the queried set (and their node attributes) and their neighbors (without any node or edge attributes). Therefore, selective harvesting is a sequential decision problem, where we must decide which node to query at each step. A classifier trained in this scenario suffers from a tunnel vision effect: without recourse to independent sampling, the urge to query promising nodes forces classifiers to gather increasingly biased training data, which we show significantly hurts the performance of AS methods and standard classifiers. We find that it is possible to collect a much larger set of targets by using multiple classifiers, not by combining their predictions as an ensemble, but switching between classifiers used at each step, as a way to ease the tunnel vision effect. We discover that switching classifiers collects more targets by (a) diversifying the training data and (b) broadening the choices of nodes that can be queried next. This highlights an exploration, exploitation, and diversification trade-off in our problem that goes beyond the exploration and exploitation duality found in classic sequential decision problems. From these observations we propose D3TS, a method based on multi-armed bandits for non-stationary stochastic processes that enforces classifier diversity, matching or exceeding the performance of competing methods on seven real network datasets in our evaluation.
The closest work to ours is on active search. The goal of active search is to uncover as many nodes of a target class as possible in a network where the topology is known @cite_7 @cite_15 @cite_6 @cite_2 . Like , active search considers situations where only members of a target class (e.g., malicious class) are sought. Since obtaining labels is associated with a cost (time or money), it is paramount to avoid spending resources on nodes that are unlikely to be targets. Unlike our problem, active search assumes the network topology is known and that any node can be queried at any time.
@cite_15 @cite_6 @cite_7 @cite_2
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We consider two active binary-classification problems with atypical objectives. In the first, active search, our goal is to actively uncover as many members of a given class as possible. In the second, active surveying, our goal is to actively query points to ultimately predict the proportion of a given class. Numerous real-world problems can be framed in these terms, and in either case typical model-based concerns such as generalization error are only of secondary importance. We approach these problems via Bayesian decision theory; after choosing natural utility functions, we derive the optimal policies. We provide three contributions. In addition to introducing the active surveying problem, we extend previous work on active search in two ways. First, we prove a novel theoretical result, that less-myopic approximations to the optimal policy can outperform more-myopic approximations by any arbitrary degree. We then derive bounds that for certain models allow us to reduce (in practice dramatically) the exponential search space required by a naive implementation of the optimal policy, enabling further lookahead while still ensuring that optimal decisions are always made. Active search is an increasingly important learning problem in which we use a limited budget of label queries to discover as many members of a certain class as possible. Numerous real-world applications may be approached in this manner, including fraud detection, product recommendation, and drug discovery. Active search has model learning and exploration exploitation features similar to those encountered in active learning and bandit problems, but algorithms for those problems do not fit active search. Previous work on the active search problem [5] showed that the optimal algorithm requires a lookahead evaluation of expected utility that is exponential in the number of selections to be made and proposed a truncated lookahead heuristic. Inspired by the success of myopic methods for active learning and bandit problems, we propose a myopic method for active search on graphs. We suggest selecting points by maximizing a score considering the potential impact of selecting a node, meant to emulate lookahead while avoiding exponential search. We test the proposed algorithm empirically on real-world graphs and show that it outperforms popular approaches for active learning and bandit problems as well as truncated lookahead of a few steps. 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.