Graph Neural Networks (GNNs) have proven to be effective in processing and
learning from graph-structured data. However, previous works mainly focused on
understanding single graph inputs while many real-world applications require
pair-wise analysis for graph-structured data (e.g., scene graph matching, code
searching, and drug-drug interaction prediction). To this end, recent works
have shifted their focus to learning the interaction between pairs of graphs.
Despite their improved performance, these works were still limited in that the
interactions were considered at the node-level, resulting in high computational
costs and suboptimal performance. To address this issue, we propose a novel and
efficient graph-level approach for extracting interaction representations using
co-attention in graph pooling. Our method, Co-Attention Graph Pooling
(CAGPool), exhibits competitive performance relative to existing methods in
both classification and regression tasks using real-world datasets, while
maintaining lower computational complexity.

提出了一种新颖高效的图级方法，通过图池化中的共同注意力来提取交互表示，该方法在使用真实世界数据集的分类和回归任务中相对于现有方法表现竞争力，同时保持较低的计算复杂度。

高效对比图交互学习的共同关注图池化

Co-attention Graph Pooling for Efficient Pairwise Graph Interaction  Learning

Humans are sophisticated at reading interlocutors' emotions from multimodal
signals, such as speech contents, voice tones and facial expressions. However,
machines might struggle to understand various emotions due to the difficulty of
effectively decoding emotions from the complex interactions between multimodal
signals. In this paper, we propose a multimodal emotion analysis framework,
InterMulti, to capture complex multimodal interactions from different views and
identify emotions from multimodal signals. Our proposed framework decomposes
signals of different modalities into three kinds of multimodal interaction
representations, including a modality-full interaction representation, a
modality-shared interaction representation, and three modality-specific
interaction representations. Additionally, to balance the contribution of
different modalities and learn a more informative latent interaction
representation, we developed a novel Text-dominated Hierarchical High-order
Fusion(THHF) module. THHF module reasonably integrates the above three kinds of
representations into a comprehensive multimodal interaction representation.
Extensive experimental results on widely used datasets, (i.e.) MOSEI, MOSI and
IEMOCAP, demonstrate that our method outperforms the state-of-the-art.

该研究提出了一种基于多模态信息的情感分析框架 ——InterMulti，并利用一种新颖的基于文本的 THHF 模块，将多种交互表现形式融合成综合的多模态交互表示来识别情感，实验结果表明，该方法的表现优于现有技术。