Modern deep transfer learning approaches have mainly focused on learning generic feature vectors from one task that are transferable to other tasks, such as word embeddings in language and pretrained convolutional features in vision. However, these approaches usually transfer unary features and largely ignore more stru…
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48 results for “graph transfer learning”
This work improves GNN training efficiency by maximizing ego-graph information.
problem Training dedicated GNNs is costly for large-scale graphs.
method Proposes EGI (Ego-Graph Information maximization) to capture essential graph information and establish a theoretical framework for transfer learning.
result Demonstrates the effectiveness of EGI in improving GNN training efficiency and transferability.
Graph few-shot learning improves node classification with prior knowledge transfer.
problem Challenging semi-supervised node classification with few labeled nodes.
method Incorporates prior knowledge from auxiliary graphs into a transferable metric space.
result Improves classification accuracy on target graphs.
HGKT transfers knowledge from seen to unseen classes in GZSL without prior unseen class info.
problem Learning to classify unseen classes in GZSL.
method Structured heterogeneous graph with graph neural network for knowledge transfer.
result Achieves state-of-the-art results on public benchmark datasets.
Paper tackles active learning for GNNs, reducing annotation costs.
problem Efficiently label nodes on graphs to reduce GNN training costs.
method Formulates as a sequential decision process, trains GNN-based policy network with reinforcement learning.
result Trains a transferable active learning policy that generalizes across different domains.
Paper proposes a new graph neural network for task-independent graph embeddings.
problem Task-dependent embeddings in graph learning domains.
method Incorporates a novel graph neural network and rich Graph Kernels for unsupervised and supervised learning.
result Consistently outperforms existing models by 3% - 8% on graph classification benchmarks.
We extend graph neural networks to transfer performance across different input sizes.
problem Transferability of graph neural networks across varying input dimensions.
method Introduce a general framework for transferability across dimensions, showing it corresponds to continuity in a limit space.
result Transferability of graph neural networks is driven by data and learning task, and can be ensured with design principles.
GCC pre-trains graph neural networks to transfer across diverse datasets.
problem Non-transferable graph models trained for specific datasets.
method Graph Contrastive Coding (GCC) framework using contrastive learning.
result GCC pre-trained models achieve competitive performance across multiple datasets.
G5 universal GRAPH-BERT learns graph representations across different datasets.
problem Learning graph representations across diverse graph datasets with distinct input and output configurations.
method G5 introduces a pluggable model architecture with input and output components for each graph data source, connected via a unified layer and fusion layer.
result G5 removes obstacles for cross-graph representation learning and transfer, even for sparse data.
This paper focuses on spectral graph convolutional neural networks (ConvNets), where filters are defined as elementwise multiplication in the frequency domain of a graph. In machine learning settings where the dataset consists of signals defined on many different graphs, the trained ConvNet should generalize to signals…
AdaGCN transfers labels across networks via adversarial domain adaptation and graph convolution.
problem Cross-network node classification with limited labeled data.
method Adversarial domain adaptation and graph convolution.
result AdaGCN successfully transfers labels with low labeled data on source networks and significant domain divergence.
Trans-GCR uses GCR model for node classification, providing theoretical guarantees and superior performance.
problem Challenges in obtaining node classification labels in real-world scenarios.
method Graph Convolutional Multinomial Logistic Regression (GCR) model and transfer learning method based on GCR.
result Trans-GCR provides superior empirical performance and theoretical guarantees.
Transfer learning which aims at utilizing knowledge learned from one problem (source domain) to solve another different but related problem (target domain) has attracted wide research attentions. However, the current transfer learning methods are mostly uninterpretable, especially to people without ML expertise. In thi…
This work extends GNNs to handle multiple graphs with non-commuting operators, proving transferability.
problem Handling multiple graphs with non-commuting operators in graph neural networks.
method Developed a mathematical theory for graph-tuple neural networks (GtNNs) with non-commuting non-expansive operators.
result Proved universal transferability of GtNNs, ensuring no non-transferable energy under convergence.
This paper focuses on spectral filters on graphs, namely filters defined as elementwise multiplication in the frequency domain of a graph. In many graph signal processing settings, it is important to transfer a filter from one graph to another. One example is in graph convolutional neural networks (ConvNets), where the…
KEEN Universe provides reproducible and transferable knowledge graph embeddings.
problem Lack of reproducibility and transferability in KGE experiments.
method Developed an ecosystem with Python packages for reproducible and transferable KGEs.
result KEEN Universe facilitates sharing of trained KGE models across different fields.
ActiLabel learns activity patterns across diverse sensor devices.
problem Limited adoption of activity recognition models across different domains due to diverse sensor devices.
method Combination of graph model and optimal tiered mapping for learning activity labels.
result Superior performance compared to state-of-the-art methods on public datasets.
Current paper addresses topology issues in PBSHM to enable transfer learning.
problem Absence of meaningful topology on graph space hinders rigorous analysis.
method Introduced parametric families of structures, defined open sets, and continuous variation between fibres.
result Enabled rigorous transfer learning in PBSHM by addressing topology issues.
Proposes a model for multi-agent reinforcement learning with hierarchical graph attention network.
problem Limited transferability of trained policies to new multi-agent tasks.
method Uses hierarchical graph attention network for representation learning and multi-agent actor-critic for policy learning.
result Demonstrates superior performance in mixed cooperative and competitive tasks compared to existing methods.
Transfer learning improves highway traffic forecasting using graph neural networks.
problem Lack of historical data for traffic forecasting on large highway networks.
method Developed a transfer learning approach for DCRNN, a graph neural network for highway forecasting.
result TL-DCRNN can forecast traffic on unseen regions of the highway network with high accuracy.
Graph neural networks can be adapted to new graphs with a limit object called graphon NNs.
problem Transferability of graph neural networks across different graphs.
method Introduced graphon NNs as limit objects of GNNs and proved a bound on the difference between GNN and graphon-NN outputs.
result The bound on the difference between GNN and graphon-NN outputs vanishes with growing number of nodes if the graph convolutional filters are bandlimited.
Graph neural networks predict future COVID-19 cases based on human mobility.
problem Predicting future COVID-19 cases using human mobility data.
method Created a graph with regions as nodes and human mobility as edge weights. Used graph neural networks to capture diffusion patterns and transfer learning for limited data.
result Graph neural networks outperform traditional methods in predicting future cases.
Unified framework predicts S&P500 index direction using transfer learning and causal graph.
problem Predicting the movement of financial indices like S&P500.
method Transfer learning, causal graph, multidisciplinary knowledge, VAE network.
result 74.3% accuracy, 67% F1-score, 0.42 Matthew correlation on 12 years test period.
Proposes a graph embedding framework for domain adaptation.
problem Improving performance on small datasets using related large datasets.
method Formulates domain adaptation as a graph embedding problem, learning a feature transformation end-to-end.
result Simple instantiation leads to state-of-the-art performance on benchmarks.
EWS-GCN improves credit scoring by analyzing money transfer connections.
problem Improving credit scoring in transactional banking data.
method Edge Weight-Shared Graph Convolutional Network (EWS-GCN) combining graph and recurrent neural networks.
result EWS-GCN outperforms state-of-the-art models in credit scoring.
Fine-tunes GNNs by preserving generative patterns to improve transferability.
problem Vanilla fine-tuning fails due to structural divergence between pre-training and downstream graphs.
method G-Tuning, which reconstructs the generative patterns of the downstream graph using graphon bases.
result G-Tuning achieves an average improvement of 0.5% and 2.6% on in-domain and out-of-domain transfer learning experiments.
Neural networks learn to execute graph algorithms.
problem Training GNNs to mimic graph algorithm steps.
method Training GNN architectures to imitate graph algorithm steps, including breadth-first search, Bellman-Ford, Prim's algorithm, and reachability.
result Maximization-based message passing neural networks are best-suited for graph algorithm decision-making.
Recent works reveal that network embedding techniques enable many machine learning models to handle diverse downstream tasks on graph structured data. However, as previous methods usually focus on learning embeddings for a single network, they can not learn representations transferable on multiple networks. Hence, it i…
GraphOpt learns the formation mechanism of graphs from observed structures.
problem Learning formation mechanisms from observed graphs with complex structural properties.
method GraphOpt uses maximum entropy inverse reinforcement learning to solve the link formation problem in a sequential decision-making process.
result GraphOpt discovers a latent objective function that can explain and transfer across different graphs.
New methods for clustering graphs using spectral analysis.
problem Graph clustering for complex systems.
method Transfer operators and spectral properties.
result Spectral clustering can be interpreted using Koopman operators.
Proposes a new model to predict polymer properties by integrating various data types.
problem Inaccurate polymer property prediction due to separate modeling of different data types.
method Multi-modal cascade feature transfer using GCN for chemical structure and molecular descriptors.
result Empirically evaluated model shows higher predictive performance than single-feature approaches.
IGNNK uses GNN for spatiotemporal kriging, improving scalability and transferability.
problem Efficiently recovering signals for unsampled locations in spatiotemporal data.
method Developed an Inductive Graph Neural Network Kriging (IGNNK) model to learn spatial message passing.
result IGNNK effectively learns spatial message passing and can be transferred to new graph structures.
This paper presents a general graph representation learning framework called DeepGL for learning deep node and edge representations from large (attributed) graphs. In particular, DeepGL begins by deriving a set of base features (e.g., graphlet features) and automatically learns a multi-layered hierarchical graph repres…
VNNs transfer well across datasets for brain age prediction using cortical thickness features.
problem Predicting brain age using anatomical features.
method Transferability of coVariance neural networks (VNNs) in brain age prediction.
result VNNs can assign anatomical interpretability to elevated brain age gap in AD.
Trans-Ising combines auxiliary datasets to estimate high-dimensional Ising models.
problem Limited target sample sizes and difficulty in using auxiliary binary datasets of unknown relevance.
method Trans-Ising uses a loss-based source screening rule and a two-stage estimation procedure.
result Trans-Ising achieves lower estimation errors than target-only estimation and naive data pooling.
G-Meta learns graph meta-learning from local subgraphs.
problem Learning from scarce data in graph tasks.
method Uses local subgraphs to transfer subgraph-specific information and learn transferable knowledge.
result G-Meta outperforms existing methods by up to 16.3% on seven datasets.
Zero-shot and few-shot learning aim to improve generalization to unseen concepts, which are promising in many realistic scenarios. Due to the lack of data in unseen domain, relation modeling between seen and unseen domains is vital for knowledge transfer in these tasks. Most existing methods capture seen-unseen relatio…
The paper bridges spectral and spatial graph convolutions, improving model capacity and transferability.
problem Improving graph neural networks by bridging spectral and spatial design.
method Theoretical demonstration and general framework for spectral analysis, new spectral convolutions, and depthwise separable convolutions.
result General framework allows spectral analysis of ConvGNNs, showing their performance and limits, and proposing new spectral convolutions.
Proposes a new method to describe graph vertex features using characteristic functions.
problem Describing the distribution of vertex features at multiple scales on graphs.
method Introduces FEATHER, a computationally efficient algorithm to calculate characteristic functions based on random walk transition probabilities.
result Demonstrates that the proposed method creates high-quality graph representations and is robust to data corruption.
Machine Learning for Scent: Learning Generalizable Perceptual Representations of Small Moleculesstat.ML
Graph neural networks improve odor prediction from molecular structure.
problem Predicting odor from molecular structure is challenging and important.
method Used graph neural networks for QSOR modeling.
result Graph neural networks significantly outperform prior methods on a novel data set.
DERWENT learns paths for distant transfer learning via deep random walk.
problem Transfer learning between distant domains is challenging.
method DERWENT uses deep random walk to explicitly learn paths between source and target domains.
result DERWENT achieves state-of-the-art performance on benchmark datasets.
A new GNN architecture called coVariance neural network (VNN) improves stability and transferability of covariance matrix analysis.
problem Stability and transferability issues in covariance matrix analysis.
method Developed coVariance neural network (VNN) that operates on sample covariance matrices.
result VNN is more stable and transferable than PCA-based approaches.
MuJAM learns traffic signal control policies that generalize to unseen intersections and traffic conditions.
problem Lack of transferability in reinforcement learning methods for traffic signal control.
method Model-based graph reinforcement learning with explicit coordination and generalization to both cyclic and acyclic constraints.
result MuJAM outperforms existing methods in zero-shot and larger transfer settings.
MetaR learns few-shot link prediction in KGs by transferring relation-specific meta info.
problem Few-shot link prediction in KGs with limited associative triples.
method MetaR framework focusing on transferring relation-specific meta information.
result MetaR achieves state-of-the-art results on few-shot link prediction benchmarks.
Machine learning predicts quantum advantage in noisy quantum walks.
problem Finding optimal graph types and coherence requirements for quantum advantage.
method Convolutional neural network trained on simulated examples of quantum walks on cycle graphs.
result Machine learning can predict quantum advantage for a wide range of decoherence parameters.
GNNs generalize CNNs for graph data, showing equivariance and stability.
problem Processing signals on graphs.
method Graph convolutional filters, nonlinearities, stacked layers.
result GNNs converge to graphon neural networks under graph convergence.
Paper proposes methods to improve graph domain adaptation by decorrelating node features.
problem Challenges in transferring knowledge from one graph to another.
method Proposes decorrelating node features using GCN and graph transformer layers.
result Significant performance enhancements and clear visualizations of learned representations.
Heterogeneous Domain Adaptation for IoT Intrusion Detection: A Geometric Graph Alignment Approachcs.CR
Geometric Graph Alignment enhances IoT intrusion detection using NID data.
problem Data scarcity hinders IoT intrusion detection accuracy.
method Geometric Graph Alignment (GGA) approach to transfer knowledge between network intrusion detection and IoT intrusion detection domains.
result GGA approach boosts IoT intrusion detection performance on multiple datasets.