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Discovering Opinion Intervals from Conflicts in Signed Graphs

NeurIPS 2025 · Graph learning › Graph algorithms and clustering · approach: Neural and local graph clustering

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Studies whether conflicts (negative edges) in a signed social graph reveal a small, interpretable set of prevalent opinion intervals.

Problem

Understanding how conflicts in online social networks reflect differing views and opinions, and whether these conflicts can be explained by a small, interpretable set of prevalent opinion intervals.

Approach

Frames opinion discovery as recovering interval structure from the conflicts (negative edges) of a signed graph, asking whether a small interpretable set of opinion intervals explains the network's conflicts. Details beyond the abstract are not available.

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