Source-linked AI summary
Multimodal learning with graphs
Yasha Ektefaie, George Dasoulas, Ayush Noori, Maha Farhat, Marinka Zitnik
TL;DR
Heterogeneous multimodal datasets require methods that accommodate modality-specific inductive biases, missing modalities, and dependencies not captured by simple fusion. The paper introduces a multimodal graph learning blueprint that organizes graph-based multimodal architectures and applications. It also identifies settings where existing methods remain limited, especially when relevant relational structure is unknown or weak.
Problem
Heterogeneous modalities have different inductive biases, may be incomplete, and can contain relational dependencies that simple fusion does not fully leverage.
Method
The MGL blueprint organizes multimodal graph learning into entity identification, topology uncovering, information propagation, and representation mixing across image-, language-, and knowledge-intensive graphs.
Results
The blueprint expresses existing multimodal graph algorithms and guides new methods across vision, language, biology, chemistry, and other natural-science applications.
Takeaways & Limitations
MGL provides a common framework for studying modality collapse and missing modalities while designing graph-based multimodal systems.
Takeaways & Limitations
Existing methods remain limited when task-relevant interactions are not known beforehand or when data lacks strong relational structure.
Abstract
from arXiv · showhide
Artificial intelligence for graphs has achieved remarkable success in modeling complex systems, ranging from dynamic networks in biology to interacting particle systems in physics. However, the increasingly heterogeneous graph datasets call for multimodal methods that can combine different inductive biases: the set of assumptions that algorithms use to make predictions for inputs they have not encountered during training. Learning on multimodal datasets presents fundamental challenges because the inductive biases can vary by data modality and graphs might not be explicitly given in the input. To address these challenges, multimodal graph AI methods combine different modalities while leveraging cross-modal dependencies using graphs. Diverse datasets are combined using graphs and fed into sophisticated multimodal architectures, specified as image-intensive, knowledge-grounded and language-intensive models. Using this categorization, we introduce a blueprint for multimodal graph learning, use it to study existing methods and provide guidelines to design new models.
1 Introduction
Multimodal graph learning addresses heterogeneous data by combining modality-specific inductive biases with graph-based cross-modal dependencies. The paper introduces the MGL blueprint as a framework for organizing architectures and applications across vision, language, and natural sciences.
- Multimodal learning combines heterogeneous descriptors to improve generalizability, robustness to distribution changes, and learning with fewer labeled examples.
- Modality collapse, missing modalities, and intricate relational dependencies make multimodal fusion difficult.
- The MGL blueprint combines modalities through graph-based dependencies and supports multimodal graph architectures across computer vision, language processing, and natural sciences.
- The blueprint organizes multimodal graph learning into identifying entities, uncovering topology, propagating information, and mixing representations.
2 Graph Neural Networks for Multimodal Learning
The MGL methodology turns multimodal inputs into downstream representations through four end-to-end components. These components separate structure learning from learning on the resulting graph structure.
- MGL provides an end-to-end blueprint that identifies entities, uncovers topology, propagates information, and mixes representations.
- Structure learning comprises entity identification and topology uncovering, whereas learning on structure comprises information propagation and representation mixing.
- Structure learning: Entity identification projects information from different modalities into a shared namespace and defines the problem’s nodes.
- Structure learning: Topology uncovering discovers interactions among entities or combines an existing graph with additional modalities.
- Learning on structure: Propagation exchanges neural messages along graph adjacencies, while mixing transforms node representations for graph-level or subgraph-level downstream tasks.
3 Multimodal Graph Learning for Images
Image-intensive graphs represent visual entities as nodes and encode spatial, feature-based, or interaction relationships as edges. MGL methods then propagate information to support visual comprehension and reasoning.
- Image-intensive graphs encode visual features as nodes and spatial connections as edges, capturing geometric priors such as translational invariance and scale separation.
- Visual comprehension: Visual comprehension tasks include image classification, segmentation, restoration, and denoising, using objects, segments, patches, and long-range context.
- Graph construction: Image graph construction identifies meaningful regions with SLIC, initializes regional features from CNNs, and connects regions using feature similarity or spatial adjacency.
- Information propagation: Graph convolutions and graph attention propagate information, with learned attention scores weighting neighboring image regions.
- Visual reasoning: Human-object interaction graphs represent body parts and objects, while visual question answering graphs connect visual, semantic, and numeric entities.
4 Multimodal Graph Learning for Language
Language-intensive graphs incorporate structure from words to documents into language models. Their task-specific topologies and propagation mechanisms support classification, relation extraction, and aspect-based sentiment analysis.
- Language contains structure at multiple levels, including syntax, sentence relations, and document relations, beyond individual words.
- Graph construction: Language-intensive graphs represent context from corpora and documents down to sentences, mentions, entities, and words.
- Language tasks: Text classification uses word-position or cooccurrence structure, while relation extraction uses dependency trees and cross-sentence entities.
- Aspect-based sentiment analysis: Aspect-based sentiment analysis requires reasoning over syntactic structure and long-range relations between aspects and other words.
- Aspect-based sentiment analysis: CoGAN models aspect-based sentiment by relating sentences sharing an aspect and neighboring sentences containing different aspects.
5 Multimodal Graph Learning in Natural Sciences
Multimodal graph learning applies knowledge-intensive graphs to natural-science problems by combining domain-specific structures, modalities, and inductive biases. Applications span particle physics, molecular chemistry, and multiscale protein modeling.
- Knowledge-intensive graphs encode scientific knowledge or task-relevant inductive biases in their structure for natural-science applications.
- Physics-informed graph neural networks integrate experimental data with physical laws to simulate systems governed by multiscale processes.They can express differential operators of underlying dynamics as functions on graph nodes.
- Molecular graph methods combine 2D structures, 3D spatial information, and stereochemistry to predict intra- and inter-molecular properties.Stereoisomers expose why spatial arrangement matters: identical connectivity can correspond to different molecular properties.
- Multiscale protein graphs integrate primary, secondary, and tertiary structures with higher-level motifs, supporting protein-ligand binding-affinity prediction.
- PERM and PERM-CAT encode chiral spatial arrangements by weighting every permutation of atoms within a chiral group during message aggregation.
- QM-GNN uses global attention to uncover interactions between atoms in separate molecules and predict whether the molecules will react.
- AlphaFold uses a knowledge-intensive graph with triangle-based updates to integrate sequence-derived representations and predict 3D protein structures.The triangle modifications encode the constraint that learned representations obey the triangle inequality on distances.
- MaSIF represents molecular protein surfaces with geometric and chemical node features and trains a graph neural network to predict protein interactions.
6 Outlook
The outlook emphasizes broader multimodal graph architectures, richer graph structures, and applications that incorporate knowledge into learning. It also identifies boundaries where prior relational structure is absent or encoded structure is less predictive than node features.
- Future progress is expected from fully multimodal graph architectures and expanded applications in natural sciences and medicine.
- The blueprint supports dependencies across images, language sequences, graphs, and tabular data, along with hypergraph and heterogeneous-graph structures.
- Knowledge-intensive graphs can support more efficient knowledge-distillation losses and visible neural networks that encode biological concepts and relationships.
- Encoding domain knowledge can improve data efficiency and model interpretability by supplying relevant principles during training.
- Existing methods are limited when relevant interactions are not known beforehand and must be automatically discovered.
- Attention with induced sparsity can discover distant interactions that local message passing cannot capture.
- When relational structure is weak, node features may be more predictive than encoded structure, and non-graph methods can perform better.
Supplementary Note 1: Overview of Graph Neural Networks
Graph neural networks learn representations by propagating information across graph neighborhoods, then transforming those representations for node-, edge-, substructure-, or graph-level prediction tasks. Their architectures differ mainly in how neighborhood information is aggregated, including convolutional, message-passing, and attention-based schemes.
- Representation levels: Graph learning represents nodes, edges, subgraphs, or entire graphs according to the prediction target.Tasks include node classification or regression, edge classification or link prediction, and graph classification or regression.
- Neural message passing: GNNs learn graph representations through differentiable information propagation between nodes and their neighborhoods.The learned node representations can be transformed into forms appropriate for downstream prediction tasks.
- GNN layer structure: A GNN layer aggregates neighborhood information, updates node states, and transforms the resulting representations into task-specific embeddings.Neighborhoods commonly consist of adjacent nodes, while final transformations can use edge operators or graph-level readout functions.
- Graph convolutions: Convolutional graph models use graph signal processing and Laplacian eigenvectors to define information propagation and aggregation.The graph convolution is expressed as x ∗g = UgU Tx, with U containing the Laplacian eigenvectors.
- Message passing: Message-passing models parameterize edge-specific message importance and combine learnable functions with aggregation operators.MPNNs and GIN use summation aggregation, while ψ may range from concatenation to a trainable neural network.
- Attention-based models: Attention-based graph models learn self-attention coefficients and may combine them with positional or spectral encodings.Examples include GAT, SAN, Graphormer, and GraphGPS.
Supplementary Note 2: Existing Methods in the MGL Blueprint
Table S1 classifies existing multimodal graph learning methods using the four components of the MGL blueprint across image-intensive, language-intensive, and knowledge-grounded graphs.
- Classification of existing methods: Table S1 identifies the four MGL components for methods spanning image-intensive, language-intensive, and knowledge-grounded graphs.The table uses dark gray, light gray, and white to distinguish these graph categories.