Sha Li

TL;DR: This paper proposes novel metrics that quantitatively measure these two circumstances and integrate them into an Adaptive-layer module and shows that allowing for node-specific aggregation degrees have significant advantage over current GNNs.

TL;DR: This work aims to develop a principled and principled frame- work that can pro le user relations as edge semantics in social networks by integrating multi-modal signals in the presence of noisy and incomplete data.

TL;DR: This article presented a new information extraction system that can automatically construct temporal event graphs from a collection of news documents from multiple sources, multiple languages (English and Spanish for their experiment), and multiple data modalities (speech, text, image and video).

TL;DR: Temporal Complex Event Schema as discussed by the authors is a graph-based schema representation that encompasses events, arguments, temporal connections and argument relations, and predicts event instances following the temporal complex event schema.