Chimaera: A Mixture-of-Graph-Experts Architecture for Cross-Task and Cross-Dataset Graph Learning

• URL: http://arxiv.org/abs/2609.08709v1
• Date: Tue, 08 Sep 2026 13:07:27 GMT
• Title: Chimaera: A Mixture-of-Graph-Experts Architecture for Cross-Task and Cross-Dataset Graph Learning
• Abstract summary: Chimaera integrates mixture-of-experts with graph foundation models (GFM)<n>It integrates different GFM architectures, such as graph prompts and linear GNN models.<n>Chimaera extends existing linear GNNs to support link-level and graph-level tasks.
• Score: 1.299941371793082
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: Designing foundation models for graphs is challenging due to the irregular structure of graphs and the different sizes and characteristics of embeddings. Chimaera integrates mixture-of-experts with graph foundation models (GFM). It integrates different GFM architectures, such as graph prompts and linear GNN models. Large language models are used to generate embeddings, and experts can be trained and combined following different strategies, GFMs, embeddings, etc. Furthermore, Chimaera extends existing linear GNNs to support link-level and graph-level tasks in addition to node-level tasks. Empirical analyses are performed on same-task and cross-task experiments with node, link, and graph classification tasks using six benchmark text-attributed graph datasets. The experiments demonstrate the effectiveness of Chimaera and its capabilities for transfer across tasks and datasets. Further insights include the need to use both large and small language models to generate embeddings for the experts, a strong cross-task transferability of simple but effective linear GNNs, and using few samples only to provide strong results.

Related papers

• Revisiting Graph Neural Networks on Graph-level Tasks: Comprehensive Experiments, Analysis, and Improvements [54.006506479865344]
  We propose a unified evaluation framework for graph-level Graph Neural Networks (GNNs)<n>This framework provides a standardized setting to evaluate GNNs across diverse datasets.<n>We also propose a novel GNN model with enhanced expressivity and generalization capabilities.
  arXiv  Detail & Related papers  (2025-01-01T08:48:53Z)
• Towards Graph Foundation Models: Learning Generalities Across Graphs via Task-Trees [50.78679002846741]
  We propose a novel approach to cross-task generalization in graphs via task-trees.<n>We show that pretraining a graph neural network (GNN) on diverse task-trees with a reconstruction objective induces transferable knowledge.<n>This enables efficient adaptation to downstream tasks with minimal fine-tuning.
  arXiv  Detail & Related papers  (2024-12-21T02:07:43Z)
• Learning From Graph-Structured Data: Addressing Design Issues and Exploring Practical Applications in Graph Representation Learning [2.492884361833709]
  We present an exhaustive review of the latest advancements in graph representation learning and Graph Neural Networks (GNNs) GNNs, tailored to handle graph-structured data, excel in deriving insights and predictions from intricate relational information. Our work delves into the capabilities of GNNs, examining their foundational designs and their application in addressing real-world challenges.
  arXiv  Detail & Related papers  (2024-11-09T19:10:33Z)
• Graph-oriented Instruction Tuning of Large Language Models for Generic Graph Mining [36.366468326267004]
  MuseGraph is a framework for graph mining across tasks and datasets.<n>It seamlessly integrates the strengths of Graph Neural Networks (GNNs) and Large Language Models (LLMs)<n>Our experimental results demonstrate significant improvements in five graph tasks and ten datasets.
  arXiv  Detail & Related papers  (2024-03-02T09:27:32Z)
• UniGraph: Learning a Unified Cross-Domain Foundation Model for Text-Attributed Graphs [30.635472655668078]
  Text-Attributed Graphs (TAGs) can generalize to unseen graphs and tasks across diverse domains.<n>We propose a novel cascaded architecture of Language Models (LMs) and Graph Neural Networks (GNNs) as backbone networks.<n>We demonstrate the model's effectiveness in self-supervised representation learning on unseen graphs, few-shot in-context transfer, and zero-shot transfer.
  arXiv  Detail & Related papers  (2024-02-21T09:06:31Z)
• Position: Graph Foundation Models are Already Here [53.737868336014735]
  Graph Foundation Models (GFMs) are emerging as a significant research topic in the graph domain. We propose a novel perspective for the GFM development by advocating for a graph vocabulary'' This perspective can potentially advance the future GFM design in line with the neural scaling laws.
  arXiv  Detail & Related papers  (2024-02-03T17:24:36Z)
• GraphMETRO: Mitigating Complex Graph Distribution Shifts via Mixture of Aligned Experts [75.51612253852002]
  GraphMETRO is a Graph Neural Network architecture that models natural diversity and captures complex distributional shifts. GraphMETRO achieves state-of-the-art results on four datasets from the GOOD benchmark.
  arXiv  Detail & Related papers  (2023-12-07T20:56:07Z)
• GraphGPT: Graph Instruction Tuning for Large Language Models [27.036935149004726]
  Graph Neural Networks (GNNs) have evolved to understand graph structures. To enhance robustness, self-supervised learning (SSL) has become a vital tool for data augmentation. Our research tackles this by advancing graph model generalization in zero-shot learning environments.
  arXiv  Detail & Related papers  (2023-10-19T06:17:46Z)
• GraphGLOW: Universal and Generalizable Structure Learning for Graph Neural Networks [72.01829954658889]
  This paper introduces the mathematical definition of this novel problem setting. We devise a general framework that coordinates a single graph-shared structure learner and multiple graph-specific GNNs. The well-trained structure learner can directly produce adaptive structures for unseen target graphs without any fine-tuning.
  arXiv  Detail & Related papers  (2023-06-20T03:33:22Z)
• Graph Mixture of Experts: Learning on Large-Scale Graphs with Explicit Diversity Modeling [60.0185734837814]
  Graph neural networks (GNNs) have found extensive applications in learning from graph data. To bolster the generalization capacity of GNNs, it has become customary to augment training graph structures with techniques like graph augmentations. This study introduces the concept of Mixture-of-Experts (MoE) to GNNs, with the aim of augmenting their capacity to adapt to a diverse range of training graph structures.
  arXiv  Detail & Related papers  (2023-04-06T01:09:36Z)
• GCC: Graph Contrastive Coding for Graph Neural Network Pre-Training [62.73470368851127]
  Graph representation learning has emerged as a powerful technique for addressing real-world problems. We design Graph Contrastive Coding -- a self-supervised graph neural network pre-training framework. We conduct experiments on three graph learning tasks and ten graph datasets.
  arXiv  Detail & Related papers  (2020-06-17T16:18:35Z)

This list is automatically generated from the titles and abstracts of the papers in this site.

This site does not guarantee the quality of this site (including all information) and is not responsible for any consequences.