The field of graph neural networks and multimodal learning is rapidly evolving, with a focus on developing more robust and generalizable models. Recent research has highlighted the importance of integrating structural information from graphs with semantic reasoning from large language models, leading to improved performance on a range of tasks such as node classification, link prediction, and graph regression. Additionally, there is a growing interest in developing models that can defend against various types of attacks, including backdoor and adversarial attacks. Noteworthy papers in this area include: UniGTE, which introduces a unified graph-text encoding framework for zero-shot generalization across graph tasks and domains. RELATE, which proposes a schema-agnostic perceiver encoder for multimodal relational graphs, achieving performance within 3% of schema-specific encoders while reducing parameter counts by up to 5x. Graph4MM, which presents a graph-based multimodal learning framework that integrates multi-hop structural information into self-attention, outperforming larger VLMs, LLMs, and multimodal graph baselines on both generative and discriminative tasks.