Graph foundation models for relational data

The digital realm is an intricate web of connections. From social networks and financial transactions to biological pathways and supply chains, data rarely exists in isolation. Instead, it forms complex relationships, creating what we call “relational data.” For decades, traditional machine learning models, while powerful, often struggled to fully capture and leverage these intrinsic connections. They typically flatten relational data into tabular formats, losing invaluable structural information in the process. This limitation has been a significant bottleneck in extracting deeper insights and making more accurate predictions in domains where relationships are paramount. However, the landscape of artificial intelligence is undergoing a profound transformation, spearheaded by the emergence of Graph Neural Networks (GNNs) and, more recently, the groundbreaking concept of Graph Foundation Models (GFMs).

The past few years have witnessed an explosion of interest and innovation in graph-centric AI. GNNs have demonstrated remarkable success in tasks like node classification, link prediction, and graph classification, proving their ability to learn rich representations directly from graph structures. Yet, like their counterparts in other domains, early GNNs were often task-specific and required substantial labeled data for training, limiting their scalability and generalizability across diverse applications. This is where the “foundation model” paradigm, popularized by large language models (LLMs) like GPT-3, has stepped in to revolutionize graph AI. Graph Foundation Models aim to extend this success to the relational data landscape. They are massive, pre-trained graph models that learn universal representations from vast, unlabeled or self-supervised graph datasets. By learning general-purpose graph knowledge during pre-training, GFMs can then be fine-tuned with minimal labeled data for a wide array of downstream tasks, dramatically reducing the data annotation burden and accelerating AI deployment. This shift promises to unlock unprecedented capabilities in understanding, predicting, and interacting with the interconnected world, driving innovation across every sector imaginable, from accelerating scientific discovery to enhancing cybersecurity. The journey into Graph Foundation Models is not just an evolutionary step but a revolutionary leap towards a more intelligent and interconnected AI future.

The Paradigm Shift: From GNNs to GFMs

The evolution from traditional Graph Neural Networks (GNNs) to Graph Foundation Models (GFMs) represents a significant paradigm shift in how we approach machine learning on relational data. While GNNs have proven adept at capturing local graph structures and node features to perform specific tasks, they often suffer from limitations related to data efficiency and generalizability. Training effective GNNs typically requires a substantial amount of labeled data for each new task or domain, a resource that is frequently scarce and expensive to acquire in real-world graph datasets. Moreover, a GNN trained for one specific task, say fraud detection in a financial network, often cannot be directly applied or easily adapted to another, such as drug discovery in a molecular graph, without significant retraining.

Graph Foundation Models address these challenges by drawing inspiration from the success of large language models (LLMs). Just as LLMs learn universal language patterns from vast text corpora, GFMs are pre-trained on massive, diverse graph datasets using self-supervised learning objectives. These objectives allow the model to learn meaningful graph representations without explicit human labels. For instance, a GFM might learn to predict masked node features, reconstruct missing links, or distinguish between real and corrupted graph structures. This pre-training phase imbues the model with a deep understanding of general graph properties, relationships, and structural patterns. The result is a highly versatile model that can be fine-tuned with a relatively small amount of labeled data for a wide range of downstream tasks, including node classification, link prediction, graph classification, and even more complex tasks like subgraph reasoning or graph generation. This approach significantly reduces the overhead of data annotation and accelerates the deployment of powerful graph-based AI solutions, making them accessible to a broader range of applications and organizations.

Core Principles of Graph Foundation Models

At their heart, GFMs operate on several core principles. First is scale: they are typically much larger than conventional GNNs, with more parameters and trained on more extensive datasets. Second is self-supervision: the ability to learn from the inherent structure of the graph itself, without requiring explicit labels. This is crucial for leveraging the vast amounts of unlabeled graph data available. Common self-supervised tasks include masked attribute prediction (reconstructing obscured node/edge features), link prediction (predicting missing or future connections), and contrastive learning (distinguishing between positive and negative graph samples). Third is transferability: the knowledge gained during pre-training is designed to be highly transferable, allowing the model to adapt efficiently to new tasks and domains with minimal fine-tuning. This dramatically reduces the “cold start” problem for new graph applications.

Addressing Data Sparsity and Label Scarcity

One of the most compelling advantages of GFMs lies in their ability to tackle the pervasive issues of data sparsity and label scarcity in graph datasets. Many real-world graphs are inherently sparse, meaning that nodes have relatively few connections compared to the total possible connections. Furthermore, obtaining high-quality labels for nodes, edges, or entire graphs is often a prohibitively expensive and time-consuming process. GFMs circumvent this by leveraging the wealth of unlabeled structural information. By learning robust, general-purpose representations during pre-training, they can capture rich semantic and structural regularities that are useful across various tasks. When fine-tuning for a specific task, the model already possesses a strong prior understanding of graph dynamics, requiring only a handful of labeled examples to specialize its knowledge. This capability democratizes advanced graph AI, enabling its application in domains where labeling resources are limited, thereby unlocking insights from previously intractable datasets. For more insights into graph data challenges, see our article on https://newskiosk.pro/.

Architecture and Key Features of GFMs

The architectural landscape of Graph Foundation Models is diverse, often drawing inspiration from advancements in natural language processing and computer vision while adapting them to the unique challenges of graph data. Unlike sequential data (text) or grid-like data (images), graphs are irregular and non-Euclidean, necessitating specialized architectures. While traditional GNN layers like Graph Convolutional Networks (GCNs) and Graph Attention Networks (GATs) form the building blocks, GFMs frequently incorporate more sophisticated mechanisms to handle large-scale learning and capture long-range dependencies. Prominent among these are Graph Transformers, which leverage self-attention mechanisms to weigh the importance of different neighbors, allowing them to capture global graph structures more effectively than localized message-passing GNNs. These architectures are designed to process and learn from the complex interplay of nodes (entities) and edges (relationships) within a graph, enabling them to construct rich, context-aware embeddings.

The true power of GFMs, however, lies not just in their architecture but also in their pre-training methodologies. These models are typically trained on massive, often heterogeneous, graph datasets spanning various domains. The pre-training objectives are carefully designed to force the model to learn fundamental properties of graphs without explicit labels. This includes tasks such as predicting masked node features or edge types, reconstructing corrupted graph structures, or using contrastive learning to differentiate between similar and dissimilar graph neighborhoods. This extensive pre-training process equips GFMs with a broad understanding of graph topology, semantic relationships, and attribute patterns. Consequently, they exhibit key features like exceptional transferability, enabling them to adapt to new tasks with minimal fine-tuning and fewer labeled examples. They are also inherently more robust to noise and missing data, as their pre-trained knowledge helps them infer missing information. Furthermore, many GFMs are designed to handle heterogeneous graphs, which contain multiple types of nodes and edges, a common characteristic of real-world relational data, allowing for a more nuanced representation of complex systems.

Pre-training Strategies and Objectives

The success of GFMs heavily hinges on effective pre-training strategies. These strategies can broadly be categorized into several types:

• Masked Attribute Prediction: Similar to BERT’s masked language modeling, GFMs might mask out certain node features or edge attributes and train the model to reconstruct them based on the surrounding graph context. This forces the model to learn rich semantic representations of nodes and edges.
• Link Prediction/Reconstruction: The model is trained to predict the existence of missing links or reconstruct original links after some are randomly removed. This helps the model understand relational patterns and infer connections.
• Contrastive Learning: This involves creating positive (similar) and negative (dissimilar) pairs of graph samples (e.g., a node and its neighborhood vs. a node and a random neighborhood) and training the model to maximize the similarity of positive pairs while minimizing that of negative pairs. This encourages the model to learn discriminative and robust embeddings.
• Graph-level Objectives: For tasks involving entire graphs, models might be pre-trained to predict graph-level properties or distinguish between different graph structures.

These objectives, often combined, ensure that the GFM learns a comprehensive understanding of graph data, from local neighborhood patterns to global structural properties. For a deeper dive into pre-training, check out https://newskiosk.pro/tool-category/upcoming-tool/.

Handling Heterogeneity and Dynamic Graphs

Real-world relational data is rarely uniform. Instead, it often involves heterogeneous graphs with multiple types of nodes (e.g., users, products, companies) and edges (e.g., purchases, friendships, employment). GFMs are increasingly designed to explicitly model this heterogeneity. Techniques like type-specific message passing, relational graph convolutions, or specialized attention mechanisms allow the model to learn distinct representations for different entity and relation types, capturing the nuanced semantics of complex systems.

Furthermore, many graphs are dynamic, evolving over time with new nodes, edges, or changing attributes (e.g., social networks, financial markets). While challenging, GFMs are beginning to incorporate mechanisms to handle temporal dynamics. This can involve using recurrent neural networks within graph layers, temporal attention mechanisms, or pre-training on sequences of graph snapshots. By effectively modeling both heterogeneity and dynamism, GFMs can provide a more accurate and comprehensive understanding of complex, evolving relational data, paving the way for applications in areas like real-time fraud detection or predictive maintenance.

Unlocking Value: Applications Across Industries

The versatility and power of Graph Foundation Models promise to be transformative across a multitude of industries, addressing complex problems that have long eluded traditional AI approaches. By understanding the intricate web of relationships within data, GFMs can uncover hidden patterns, predict future states, and optimize systems in ways previously unimaginable. The ability to learn from vast, unlabeled graph structures and then adapt to specific tasks with minimal labeled data makes them incredibly valuable for domains rich in relational data but sparse in labels.

In the realm of healthcare and life sciences, GFMs are poised to revolutionize drug discovery and precision medicine. Molecular structures are inherently graphs, and by modeling interactions between atoms and compounds, GFMs can predict drug-target interactions, identify novel drug candidates, and even accelerate the repurposing of existing drugs. Beyond molecules, GFMs can analyze patient similarity networks, electronic health records (EHRs) linked by diagnoses or treatments, or protein-protein interaction networks to identify disease mechanisms, predict disease progression, and personalize treatment plans. The interconnectedness of biological data makes GFMs an ideal tool for unraveling complex biological systems.

In the financial sector, GFMs are becoming indispensable for advanced fraud detection and risk assessment. Financial transactions naturally form a graph where accounts are nodes and transactions are edges. GFMs can detect anomalous patterns indicative of fraud rings, money laundering, or other illicit activities by analyzing the flow of money and the relationships between entities far more effectively than rule-based systems or traditional tabular models. They can also be used for credit scoring, identifying systemic risks in interconnected markets, and optimizing portfolio management by understanding correlations between assets.

E-commerce and recommendation systems are another prime area for GFM application. User-item interaction graphs are fundamental to personalized recommendations. GFMs can model complex user preferences, item relationships (e.g., complementary products, substitute goods), and even social influence among users to provide highly accurate and contextualized recommendations, significantly boosting engagement and sales. Similarly, in supply chain management, GFMs can optimize logistics, predict disruptions, and identify vulnerabilities by modeling the intricate network of suppliers, manufacturers, distributors, and retailers.

Revolutionizing Drug Discovery and Bioinformatics

The process of drug discovery is notoriously long, expensive, and high-risk. GFMs can significantly accelerate several stages. By representing molecules as graphs, they can predict their properties (e.g., toxicity, efficacy), synthesize new molecular structures with desired characteristics, and identify potential drug candidates by modeling how they bind to target proteins. This involves learning from vast databases of known compounds and their interactions. Furthermore, in bioinformatics, GFMs can analyze protein-protein interaction networks to understand disease mechanisms, interpret genomic data by modeling gene regulatory networks, and even predict the function of unknown proteins, leading to breakthroughs in understanding complex biological processes. The ability of GFMs to learn from sparse and heterogeneous biological data is a game-changer for advancing scientific research.

Fortifying Financial Systems Against Fraud

Financial fraud is a multi-billion dollar problem globally. Traditional methods often struggle with sophisticated, evolving fraud schemes that involve multiple actors and complex transaction patterns. GFMs excel here because they can inherently model these relationships. By representing financial entities (accounts, individuals, merchants) as nodes and transactions as edges, GFMs can identify unusual clusters, circular transaction patterns, or hidden connections that signify fraudulent activity. For example, a GFM can detect a fraud ring by identifying a group of seemingly unrelated accounts engaging in similar suspicious transaction patterns, even if individual transactions appear normal. This relational intelligence allows GFMs to catch novel fraud schemes faster and with greater accuracy, significantly enhancing the security and integrity of financial systems. Explore more on AI in finance here: https://newskiosk.pro/tool-category/upcoming-tool/.

Navigating the Landscape: Challenges and Limitations

While Graph Foundation Models hold immense promise, their widespread adoption and full potential are still tempered by several significant challenges and limitations. These obstacles span computational resources, data management, ethical considerations, and the inherent complexity of graph data itself. Addressing these issues is critical for the continued progress and successful deployment of GFMs across various industries.

One of the most pressing challenges is scalability and computational cost. Training foundation models, whether for language, vision, or graphs, requires enormous computational power. GFMs, especially when dealing with graphs containing billions of nodes and trillions of edges, demand vast amounts of memory and processing capabilities. The irregular nature of graph data makes parallelization and batch processing more complex than with grid-like data, leading to computationally intensive message-passing operations. Deploying these large models in real-time or resource-constrained environments also presents significant hurdles. Furthermore, collecting and curating truly massive, diverse, and clean graph datasets suitable for pre-training GFMs is a monumental task, often requiring sophisticated data engineering pipelines and significant storage infrastructure.

Another critical area of concern revolves around data privacy and ethical implications. Graph data, by its very nature, often contains highly sensitive information about individuals, organizations, and their relationships. Training large GFMs on such data raises serious privacy concerns, as the models could potentially inadvertently memorize or reconstruct sensitive details. Ensuring differential privacy, anonymization, and adherence to data governance regulations (like GDPR) becomes paramount. Moreover, the inherent “black box” nature of deep learning models, including GFMs, poses challenges for interpretability. Understanding *why* a GFM made a particular prediction or flagged a specific connection as suspicious is crucial for critical applications like healthcare or finance, where explainability is not just desirable but often legally mandated. The risk of bias amplification, where biases present in the training data are learned and propagated by the model, is also a significant ethical consideration, potentially leading to unfair or discriminatory outcomes.

Scalability and Computational Demands

The computational demands of GFMs are formidable. Training models with hundreds of millions or even billions of parameters on graphs with billions of nodes and edges requires specialized hardware (GPUs, TPUs), distributed computing frameworks, and optimized graph processing libraries. Storing these massive graphs and their embeddings in memory during training can quickly exhaust available resources. Inference, while generally less demanding than training, can still be computationally intensive for large graphs, especially when real-time predictions are required. Researchers are actively working on solutions like graph sampling techniques, hierarchical graph representations, and efficient distributed GNN training algorithms to mitigate these challenges, but scalability remains a bottleneck for truly universal GFMs. https://7minutetimer.com/tag/markram/ provides more context on scaling GNNs.

Ethical Implications and Interpretability

The ethical considerations surrounding GFMs are multifaceted. Beyond data privacy, the potential for bias amplification is a serious concern. If the training data reflects societal biases (e.g., certain demographic groups being overrepresented or underrepresented in fraud datasets), the GFM can learn and perpetuate these biases, leading to unfair outcomes. Developing techniques for bias detection, mitigation, and fairness-aware graph learning is crucial.

Interpretability is another critical limitation. When a GFM recommends a drug candidate or flags a transaction as fraudulent, stakeholders need to understand the reasoning. Black-box models hinder trust and accountability. Current research focuses on developing methods to extract explanations from GFMs, such as identifying the most influential nodes or edges contributing to a prediction, visualizing learned embeddings, or using counterfactual explanations. Achieving a balance between model complexity (for performance) and interpretability (for trust and accountability) is an ongoing challenge in GFM development.

The Future of Relational AI with GFMs

The trajectory of Graph Foundation Models points towards an exciting future, where AI gains an increasingly sophisticated understanding of interconnected data. We are at the cusp of a new era for relational AI, moving beyond mere pattern recognition to deeper reasoning and more holistic intelligence. The ongoing research and development in this field are rapidly pushing the boundaries of what’s possible, promising to unlock unprecedented capabilities across science, industry, and society.

One of the most promising avenues is the development of multimodal graph intelligence. Current GFMs primarily focus on graph structure and node/edge attributes. However, real-world entities often come with diverse data modalities – text descriptions, images, audio, and time series data, all interconnected within a graph. Imagine a GFM that can process the text of a research paper, the image of a molecule, and the graph of citations to accelerate scientific discovery. Integrating these diverse data types into a unified graph representation and model architecture will enable GFMs to build a far richer and more nuanced understanding of complex systems. This convergence will lead to GFMs that can reason across different data types, performing tasks like generating textual summaries of graph structures or identifying visual patterns linked to specific graph topologies.

Furthermore, the future of GFMs will likely involve enhanced capabilities for causal inference and reasoning. While current GFMs are excellent at correlation and prediction, understanding causality – why certain events happen or how interventions might change outcomes – remains a significant challenge. By modeling causal relationships within graphs (e.g., A causes B, not just A is correlated with B), GFMs could move beyond predictive analytics to prescriptive analytics, offering actionable insights for decision-making. Imagine a GFM that can not only predict potential supply chain disruptions but also recommend the optimal interventions to mitigate them, understanding the ripple effects of each decision. This deeper reasoning capability will transform GFMs from powerful predictive tools into intelligent decision-making engines.

Towards Multimodal Graph Intelligence

The fusion of graph learning with other AI domains represents a significant frontier. Efforts are underway to build GFMs that can jointly embed information from graphs and other modalities. For example, Graph-Text models could learn representations from knowledge graphs and their corresponding textual descriptions, enabling more accurate question-answering on structured data or generating natural language descriptions of complex networks. Similarly, Graph-Image models could analyze images of biological samples within a patient similarity graph to better diagnose diseases. This multimodal integration will create a new class of “holistic” foundation models capable of understanding and reasoning about the world through its interconnected entities and their diverse attributes. The ability to cross-reference and synthesize information from different modalities within a unified graph framework will lead to more robust, comprehensive, and intelligent AI systems. https://7minutetimer.com/tag/aban/ provides examples of multimodal graph models.

The Path to General Relational AI

Ultimately, the vision for GFMs is to contribute to the development of General Relational AI – systems that can understand, reason about, and interact with arbitrary relational data in a flexible and human-like manner. This involves not just better prediction but also capabilities like symbolic reasoning, planning, and adaptive learning on graph structures. Future GFMs might be able to generate novel graph structures (e.g., new molecules, network designs), perform complex multi-hop reasoning over knowledge graphs, or even learn to interact with graph environments through reinforcement learning. The continuous advancements in scalable GNN architectures, self-supervised learning techniques, and the integration of diverse data modalities are paving the way for these more sophisticated capabilities. As research progresses, GFMs will become increasingly intelligent agents for navigating and interpreting the complex, interconnected world we live in, ultimately empowering us to make better decisions and solve more complex problems. https://7minutetimer.com/web-stories/learn-how-to-prune-plants-must-know/ discusses the future of foundation models.

Comparison of Graph AI Tools and Techniques

Here’s a comparison of various tools, models, or techniques relevant to Graph Foundation Models:

Model/Technique	Core Idea	Key Advantages	Best Use Case	Limitations
Traditional GNNs (e.g., GCN, GAT)	Message passing to aggregate neighbor information for node/graph embeddings.	Good for capturing local graph structure; relatively simple to implement.	Node classification, link prediction on smaller, homogeneous graphs.	Limited generalization; requires labeled data; struggles with long-range dependencies and very large graphs.
Graph Transformers	Adapts self-attention mechanism from Transformers to graph data.	Captures long-range dependencies effectively; better global understanding; handles varying graph sizes.	Complex relational reasoning, graph classification, where global context is important.	High computational cost (quadratic complexity to nodes); memory intensive for very large graphs.
GraphMAE (Masked Autoencoders for Graphs)	Self-supervised pre-training by masking node features/edges and reconstructing them.	Excellent for learning general graph representations from unlabeled data; strong transferability.	Pre-training foundation models; few-shot learning; cold-start problems.	Requires large unlabeled datasets for effective pre-training; computational cost of pre-training.
Knowledge Graphs (KGs)	Structured representation of entities and their relationships as triples (subject, predicate, object).	Human-readable; enables symbolic reasoning; explicit semantics; can integrate diverse data.	Question answering, recommendation systems, semantic search, data integration.	Manual curation is expensive; incompleteness; integrating with sub-symbolic models (GNNs) can be complex.
Open Graph Benchmark (OGB)	A suite of challenging, large-scale, and diverse graph datasets and benchmarks.	Standardized evaluation; fosters reproducibility; accelerates research in scalable graph learning.	Benchmarking new GNN/GFM architectures; large-scale graph learning research.	Not a model itself, but a resource for model development; datasets can still be very large to process.

Expert Tips and Key Takeaways

• Start with Data Quality: The performance of any GFM hinges on the quality and structure of your input graph data. Ensure nodes, edges, and their attributes are accurately defined and clean.
• Leverage Pre-trained Models: Whenever possible, utilize existing pre-trained Graph Foundation Models. Fine-tuning is often more efficient than training from scratch.
• Understand Your Graph Structure: Different graph types (homogeneous, heterogeneous, directed, undirected) require different modeling considerations. Tailor your GFM choice to your data’s intrinsic structure.
• Embrace Self-Supervised Learning: If pre-trained GFMs aren’t available for your specific domain, explore self-supervised pre-training on your unlabeled graph data to build a domain-specific foundation.
• Mind the Scale: Be mindful of the computational resources required for large-scale GFM training and inference. Consider graph sampling or distributed computing for massive graphs.
• Focus on Interpretability: Especially in critical applications, explore methods to interpret GFM predictions. Understanding ‘why’ is as important as ‘what’.
• Consider Heterogeneity: Real-world graphs are often heterogeneous. Choose GFMs capable of handling multiple node and edge types to capture richer semantics.
• Stay Updated on Architectures: The field of graph AI is rapidly evolving. Keep an eye on new GFM architectures like Graph Transformers and their advancements.
• Benchmark Rigorously: Use standardized benchmarks like OGB to evaluate your GFM’s performance and ensure fair comparisons.
• Address Ethical Concerns: Be proactive in considering data privacy, bias, and fairness implications when working with sensitive graph data and GFMs.

Frequently Asked Questions (FAQ)

What is relational data, and why is it important for AI?

Relational data refers to information that is inherently connected, where entities (nodes) interact or relate to each other through relationships (edges). Examples include social networks, financial transactions, molecular structures, and supply chains. It’s crucial for AI because many real-world problems are best understood by analyzing these connections, not just individual data points. Traditional AI models often struggle to capture these complex relationships, leading to suboptimal insights.

How do Graph Foundation Models (GFMs) differ from traditional Graph Neural Networks (GNNs)?

GFMs are an evolution of GNNs. The primary difference lies in their scale and pre-training strategy. Traditional GNNs are typically trained from scratch for a specific task with labeled data. GFMs, like large language models, are massive GNNs pre-trained on vast, unlabeled graph datasets using self-supervised learning objectives. This pre-training allows them to learn general-purpose graph knowledge, making them highly adaptable and transferable to a wide range of downstream tasks with minimal fine-tuning and fewer labels.

What industries can benefit most from Graph Foundation Models?

Industries rich in relational data stand to benefit significantly. This includes healthcare (drug discovery, patient similarity), finance (fraud detection, risk assessment), e-commerce (recommendation systems, user behavior analysis), social media (content recommendation, community detection), logistics (supply chain optimization), and cybersecurity (threat detection). Any domain where understanding connections and relationships is key to insights and predictions is a prime candidate.

Are there open-source Graph Foundation Models available for use?

The field is rapidly evolving, and while some large proprietary GFMs exist, the open-source landscape is growing. Projects often release pre-trained GNN models that can serve as foundational components, and research papers frequently share model architectures and pre-training strategies. Platforms like PyTorch Geometric and DGL provide the necessary frameworks to build and experiment with GFMs. As the field matures, we can expect more fully fledged, openly available GFMs.

What are the main challenges in deploying Graph Foundation Models?

The primary challenges include the immense computational resources required for training and inference on massive graphs, the difficulty of collecting and curating large-scale, diverse graph datasets, and the inherent complexity of managing and processing graph data. Additionally, ethical concerns around data privacy, potential biases in training data, and the interpretability of complex graph models pose significant hurdles for real-world deployment.

Can GFMs handle dynamic graphs that change over time?

Yes, the research and development in GFMs are increasingly focusing on handling dynamic graphs. This involves designing architectures that can incorporate temporal information, such as recurrent graph layers or temporal attention mechanisms. Pre-training strategies can also be adapted to learn from sequences of graph snapshots or by predicting future graph states. While challenging, dynamic GFMs are crucial for applications in real-time evolving systems like social networks or financial markets.

The journey into Graph Foundation Models is just beginning, yet its potential to reshape how we interact with and understand relational data is undeniable. From revolutionizing scientific discovery to fortifying our digital infrastructure, GFMs are poised to become a cornerstone of next-generation AI.

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