IM-META: Influence Maximization Using Node Metadata in Networks With Unknown Topology

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
$\mathsf{IM\!-\!META}$
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.