  Multimodal data pervades various domains, including healthcare, social media,
and transportation, where multimodal graphs play a pivotal role. Machine
learning on multimodal graphs, referred to as multimodal graph learning (MGL),
is essential for successful artificial intelligence (AI) applications. The
burgeoning research in this field encompasses diverse graph data types and
modalities, learning techniques, and application scenarios. This survey paper
conducts a comparative analysis of existing works in multimodal graph learning,
elucidating how multimodal learning is achieved across different graph types
and exploring the characteristics of prevalent learning techniques.
Additionally, we delineate significant applications of multimodal graph
learning and offer insights into future directions in this domain.
Consequently, this paper serves as a foundational resource for researchers
seeking to comprehend existing MGL techniques and their applicability across
diverse scenarios.
