Moreover,compellingevidenceshowsthat GNNs are extremely vulnerable to graph data shifts [79,26,20].

Note that the shift components can be customized and expanded based on downstream tasks.

Graph data are inherently complex and heterogeneous, leading to a high natural diversity of distributional shifts. However, it remains unclear how to build machine learning architectures that generalize to complex non-synthetic distributional shifts naturally occurring in the real world. Here we develop GraphMETRO, a Graph Neural Network architecture, that reliably models natural diversity and captures complex distributional shifts. GraphMETRO employs a Mixture-of-Experts (MoE) architecture with a gating model and multiple expert models, where each expert model targets a specific distributional shift to produce a shift-invariant representation, and the gating model identifies shift components. Additionally, we design a novel objective that aligns the representations from different expert models to ensure smooth optimization. GraphMETRO achieves state-of-the-art results on four datasets from GOOD benchmark comprised of complex and natural real-world distribution shifts, improving by 67% and 4.2% on WebKB and Twitch datasets.

Emotion Recognition in Conversations (ERC) is an important and active research problem. Recent work has shown the benefits of using multiple modalities (e.g., text, audio, and video) for the ERC task. In a conversation, participants tend to maintain a particular emotional state unless some external stimuli evokes a change. There is a continuous ebb and flow of emotions in a conversation. Inspired by this observation, we propose a multimodal ERC model and augment it with an emotion-shift component. The proposed emotion-shift component is modular and can be added to any existing multimodal ERC model (with a few modifications), to improve emotion recognition. We experiment with different variants of the model, and results show that the inclusion of emotion shift signal helps the model to outperform existing multimodal models for ERC and hence showing the state-of-the-art performance on MOSEI and IEMOCAP datasets.