arXiv AI
Jun 11
MLaGA: Multimodal Large Language and Graph Assistant
arXiv:2506. 02568v2 Announce Type: replace Abstract: Large Language Models (LLMs) have demonstrated substantial efficacy in advancing graph-structured data analysis.
MOVE: Multimodal Open-world Verification and Expansion for Graph Learning
The paper introduces MOVE, a framework for multimodal open‑world verification and expansion in graph learning. MOVE jointly uses visual tokens, textual attributes, and graph context to identify nodes that cannot be assigned to existing classes, then employs a multimodal LLM to generate candidate class descriptions. It selectively expands the class space only when multimodal evidence consistently supports the new classes, avoiding redundancy, and reports an average 11.87% improvement across unknown recognition, open‑domain annotation, and downstream graph learning tasks.
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arXiv Machine Learning
Jul 22
One Model, Many Graphs: Learning over Attributed Graphs across Heterogeneous Modalities with Vision-Language Models
arXiv:2607. 19128v1 Announce Type: new Abstract: Vision-language models (VLMs) provide a unified representation space for textual and visual information, yet their potential as general-purpose backbones for graph-structured data remains largely unexplored.
arXiv Machine Learning
Jun 17
Multimodal Graph Negative Learning
arXiv:2606. 12863v2 Announce Type: replace Abstract: Multimodal attributed graphs (MAGs) integrate graph topology with heterogeneous modality attributes, such as text and images, thereby enabling richer modeling of complex relational systems.
arXiv AI
Jul 29
CHARM: A Multimodal Graph Foundation Model with Hierarchical Context Modeling for Zero-Shot Transfer
arXiv:2607. 26023v1 Announce Type: new Abstract: Graph foundation models (GFMs) have emerged as a promising paradigm for transferring knowledge across graph domains and tasks.
arXiv Machine Learning
4d ago
The Universal Classifier for Graph Learning
arXiv:2609.36302v1 Announce Type: new Abstract: While foundation models have revolutionized natural language processing and computer vision by leveraging universal vocabularies, Graph Machine Learnin...
arXiv Machine Learning
Jul 31
FedOGL: Combating Catastrophic Forgetting in Federated Open-World Multimodal Graph Learning
arXiv:2607. 27665v1 Announce Type: new Abstract: Federated graph learning enables collaborative training over decentralized graph data without sharing raw graph information.
arXiv Machine Learning
Jul 1
FedLAB: Traceable Semantic Codebooks for Federated Multimodal Graph Foundation Learning
arXiv:2606. 32016v1 Announce Type: new Abstract: Multimodal graph foundation models aim to learn reusable knowledge from graphs enriched with text, images, attributes, and relational topology, thereby supporting diverse graph-centric and modality-centric tasks.
arXiv Machine Learning
Aug 3
MMFGU: Multimodal Federated Graph Unlearning
arXiv:2607. 28708v1 Announce Type: new Abstract: Multimodal federated graph learning enables clients to collaboratively train graph models over structural, textual, and visual signals without sharing private local data.
arXiv Machine Learning
Jun 19
Towards Modality-imbalanced Federated Graph Learning: A Data Synthesis-based Approach
arXiv:2606. 20382v1 Announce Type: new Abstract: MultiModal Federated Graph Learning (MM-FGL) offers a natural collaborative training paradigm, but its practical deployment is challenged by two granularities of modality imbalance.
arXiv Machine Learning
Aug 10
TMTE: Effective Multimodal Graph Learning with Task-aware Modality and Topology Co-evolution
arXiv:2603. 27723v2 Announce Type: replace Abstract: Multimodal-attributed graphs (MAGs) are a fundamental data structure for multimodal graph learning (MGL), enabling both graph-centric and modality-centric tasks.
arXiv Machine Learning
Sep 4
LLM as GNN: Graph Vocabulary Learning for Text-Attributed Graph Foundation Models
The paper introduces PromptGFM, a Graph Foundation Model designed for text‑attributed graphs (TAGs). It integrates Large Language Models (LLMs) and Graph Neural Networks (GNNs) through a Graph Understanding Module that prompts LLMs to emulate GNN workflows, and a Graph Inference Module that creates a language‑based graph vocabulary for better alignment and scalability. Experiments show PromptGFM outperforms existing methods and transfers effectively across various graphs and tasks.
arXiv AI
Jul 28
Multimodal Semantic-Probabilistic Objectness for Open World Object Detection
arXiv:2607. 23981v1 Announce Type: cross Abstract: Open-world object detection (OWOD) requires a detector to recognize known categories, discover unnamed objects from unseen categories, and incrementally learn newly annotated classes.