How can we detect a group of individuals whose connectivity persists and even strengthens over time? Despite extensive research on temporal networks, this practically pertinent question has been scantily investigated. In this paper, we formulate the problem of selecting a subset of nodes whose induced subgraph maximizes the overall edge count while abiding by time-aware spectral connectivity constraints. We solve the problem via a semidefinite programming (SDP) relaxation. Our experiments on a broad array of synthetic and real-world data establish the effectiveness of our method and deliver key insights on real-world temporal graphs.
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Petsinis et al. (2024) studied this question.
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