A Bregman ADMM module imposes sparse and low-rank structure on responsibility and attention matrices in temporal point processes, improving performance and interpretability of event branch inference.
Who Started It? Identifying Root Sources in Textual Conversation Threads
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abstract
In textual conversation threads, as found on many popular social media platforms, each particular user text comment either originates a new thread of discussion, or replies to a previous comment. An individual who makes an original comment ---termed as the "root source''---is a topic initiator or even an information source, and identifying such individuals is of particular interest. The reply structure of comments is not always available (e.g. in the proliferation of a news event), and thus identifying root sources is a nontrivial task. In this paper, we develop a generative model based on marked multivariate Hawkes processes, and introduce a novel concept, "root source probability", to quantify the uncertainty in attributing possible root sources to each comment. A dynamic-programming-based algorithm is then derived to efficiently compute root source probabilities. Experiments on synthetic and real-world data show that our method identifies root sources that match ground truth and human intuition.
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cs.LG 1years
2025 1verdicts
CONDITIONAL 1representative citing papers
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A Plug-and-Play Bregman ADMM Module for Inferring Event Branches in Temporal Point Processes
A Bregman ADMM module imposes sparse and low-rank structure on responsibility and attention matrices in temporal point processes, improving performance and interpretability of event branch inference.