Graph4MM: Graph-based Multimodal Learning

• Graph4MM is a graph-based multimodal learning framework that integrates graph topology with text and images to capture complex, multi-hop semantic relationships.
• Its Hop-Diffused Attention mechanism propagates information across multi-hop neighbors, preserving feature variance and preventing over-smoothing in deep models.
• The MM-QFormer module enables effective cross-modal fusion, significantly improving generative and discriminative task performance over conventional methods.

Graph4MM is a graph-based multimodal learning framework designed to leverage structural information present in real-world data for enhanced multimodal reasoning. Unlike prior approaches that flatten modality relationships or treat graphs as a separate data channel, Graph4MM directly integrates graph topology into foundation models, weaving together text, image, and structure in the same representational pipeline. Its central innovations—the Hop-Diffused Attention mechanism and the MM-QFormer module—enable the principled fusion of intra- and inter-modal information across multi-hop connections, significantly improving performance on both generative and discriminative tasks.

1. Motivation and Conceptual Foundation

Graph4MM was developed in response to shortcomings in existing multimodal systems, which typically model only simple, one-to-one mappings (e.g., image-caption pairs) or treat structural graph data as an independent modality, detached from textual or visual representations. This ignores complex, many-to-many interactions among entities and fails to distinguish between multi-hop neighbors, resulting in fragmented semantic understanding. Real-world multimodal data—such as webpages, scientific documents, or product networks—feature intricate contextual dependencies (inter-modal co-references, structural relations) that require more nuanced modeling. Graph4MM addresses these gaps by embedding structural relationships within the attention mechanism and fusing modality-specific features under explicit graph-guided supervision.

2. Hop-Diffused Attention Mechanism

The Hop-Diffused Attention module operationalizes the inclusion of multi-hop graph structure into transformer self-attention. Standard self-attention computes weights ignoring graph connectivity; Graph4MM introduces two key modifications:

• Causal Masking: For each node pair $(v_i, v_j)$, a mask $M_{i,j}$ is set to 1 if an edge exists, 0 otherwise:

$$M_{i,j} = \begin{cases} 1 & \text{if}~(v_i,v_j) \in \mathcal{E} \ 0 & \text{else} \end{cases}$$

Masked attention scores become $A_{i,j} = \mathrm{Softmax}(M_{i,j} \cdot A'_{i,j})$.

• Hop Diffusion: To capture information beyond immediate neighbors, the attention matrix is diffused via an infinite weighted sum of its powers:

$$\mathcal{A} = \sum_{i=0}^\infty \theta_i A^i, \quad \theta_i = \alpha(1-\alpha)^i, \quad \sum_{i=0}^\infty \theta_i = 1$$

Practically, this sum is truncated to $K$ hops. Node embeddings (for any modality) are then updated by residual aggregation:

$H \leftarrow H + \mathcal{A} H$

This design ensures that distant nodes contribute information decaying exponentially with hop count. Theoretical analysis demonstrates that this diffused process preserves feature variance (higher Dirichlet energy) than standard GNNs (e.g., GAT), avoiding over-smoothing that occurs in deep or multi-hop aggregation.

3. MM-QFormer for Cross-Modal Fusion

MM-QFormer is a specialized querying transformer to address cross-modal fusion in the presence of structural information. Its operation comprises:

• Shared Self-Attention: Learnable query tokens are co-attended with textual embeddings, integrating language context.
• Modality Cross-Attention: In a subsequent step, query tokens cross-attend over structure-enhanced visual embeddings (which have undergone hop-diffused attention). This staged, multi-mapping attention allows selective extraction of relevant visual features informed by topology and language.
• Feed-Forward Refinement and Token Concatenation: The processed query tokens are concatenated with the text tokens and forwarded to the final pre-trained LLM.

Unlike traditional token-concatenator approaches, MM-QFormer explicitly orchestrates modality interaction under graph supervision, improving both semantic precision and contextual understanding.

4. Theoretical Properties

Graph4MM is supported by several key theoretical findings:

• Dirichlet Energy Preservation: Proposition 1 shows that standard GNN aggregations (e.g., k-hop GAT) cause the Dirichlet energy—the proxy for feature variance—to decay exponentially, resulting in nearly identical node features (over-smoothing). In contrast, Hop-Diffused Attention maintains higher Dirichlet energy, as proved by its weighted sum design.
• Mutual Information Alignment: Proposition 2 analyzes the mutual information gap between small-scale GCN graph embeddings and high-complexity pretrained language/vision model features, demonstrating that treating graphs as a stand-alone modality introduces significant information misalignment. By embedding graph structure as an inductive bias (not as a modality), Graph4MM preserves more mutual information, aiding effective fusion.

5. Empirical Performance

Graph4MM is validated on both generative and discriminative tasks:

Task Type	Dataset	Metric	Graph4MM Performance	Improvement over Baselines
Generative	WikiWeb2M	BLEU-4, ROUGE-L	Up to 6.93% higher on average	>6% over VLMs and LLMs
Discriminative	Ele-Fashion	Accuracy, Recall	Near-perfect scores, some at 100%	Strong improvement over MMGL

Experiments span document section summarization, product classification, and recommendation scenarios, consistently showing superior performance over state-of-the-art VLMs, LLMs, and multimodal graph learning baselines. Empirical analysis supports the claim that structure-guided multimodal fusion yields substantial gains.

6. Applications and Future Directions

Graph4MM is applicable to domains where multimodal data exhibits rich relational complexity, including:

• Document Understanding: Generating or summarizing content by leveraging cross-section dependencies among text blocks, images, and captions.
• E-Commerce and Recommendation Systems: Classifying products or recommending items by exploiting both visual/textual attributes and co-purchase graph relationships.
• Scientific Literature and Knowledge Graphs: Enabling more nuanced interactions among publication elements (figures, tables, sections) and external references.

Future work will address scaling Graph4MM to larger, more complex graph structures, extending fusion mechanisms to new modalities and edge types, and adapting for tasks like link prediction or open-domain reasoning. Further, enhancing multi-hop structural modeling and dynamic graph adaptation are proposed as promising advancements.

7. Summary and Significance

Graph4MM redefines multimodal learning by structurally integrating graph topologies into the fusion of text and visual modalities. Its two central components—Hop-Diffused Attention and MM-QFormer—move beyond standalone graph and modality modeling, offering structure-guided, multi-hop, cross-modal fusion for foundation models. Theoretical and empirical analysis demonstrates that this approach improves semantic understanding and information propagation, especially in tasks demanding context-aware reasoning. Graph4MM thus constitutes a principled framework for leveraging complex graph structures in multimodal learning, setting the stage for next-generation models that can better handle the intricacies of real-world multimodal data (Ning et al., 19 Oct 2025).