  Since the structure of complex networks is often unknown, we may identify the
most influential seed nodes by exploring only a part of the underlying network,
given a small budget for node queries. We propose IM-META, a solution to
influence maximization (IM) in networks with unknown topology by retrieving
information from queries and node metadata. Since using such metadata is not
without risk due to the noisy nature of metadata and uncertainties in
connectivity inference, we formulate a new IM problem that aims to find both
seed nodes and queried nodes. In IM-META, we develop an effective method that
iteratively performs three steps: 1) we learn the relationship between
collected metadata and edges via a Siamese neural network, 2) we select a
number of inferred confident edges to construct a reinforced graph, and 3) we
identify the next node to query by maximizing the inferred influence spread
using our topology-aware ranking strategy. Through experimental evaluation of
IM-META on four real-world datasets, we demonstrate a) the speed of network
exploration via node queries, b) the effectiveness of each module, c) the
superiority over benchmark methods, d) the robustness to more difficult
settings, e) the hyperparameter sensitivity, and f) the scalability.
