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.
Proposes a deep learning model for timely and accurate recommendations.
problem Inability to provide timely recommendations and ranking issues with implicit feedback.
method Unified cross-network solution using listwise ranking for implicit data.
result Superior performance in accuracy, novelty, and diversity compared to baselines.
Graph cross network improves graph classification accuracy.
problem Improving graph classification accuracy.
method Graph cross network (GXN) with vertex infomax pooling (VIPool) and feature-crossing layer.
result Improves graph classification accuracy by 2.12% and 1.15%.
Feature engineering has been the key to the success of many prediction models. However, the process is non-trivial and often requires manual feature engineering or exhaustive searching. DNNs are able to automatically learn feature interactions; however, they generate all the interactions implicitly, and are not necessa…
We use a principal-agent model to analyze the structure of a book-driven dealer market when the dealer faces competition from a crossing network or dark pool. The agents are privately informed about their types (e.g. their portfolios), which is something that the dealer must take into account when engaging his counterp…
LSTM improves cross-network recommendations by capturing user preference changes and irregular time intervals.
problem Offline cross-network recommender solutions fail to capture user preference changes and dynamic environments.
method Proposes a multi-layered LSTM network with attention mechanisms, higher order interactions, and time-aware gates.
result The model consistently outperforms state-of-the-art in accuracy, diversity, and novelty.
CnGAN generates synthetic user preferences for non-overlapped users in cross-network recommender systems.
problem Cross-network recommender solutions ignore non-overlapped users, limiting their applicability.
method Multi-task learning, encoder-GAN architecture, user-based pairwise loss function.
result Generated user preferences improve recommendations for non-overlapped users, achieving superior performance.
DCN-V2 improves deep & cross network for web-scale learning to rank systems.
problem Efficiently learning feature interactions in large-scale recommender systems.
method Proposes DCN-V2, an improved framework for deep & cross network learning.
result DCN-V2 outperforms state-of-the-art algorithms on benchmark datasets.
A new method improves recommendation accuracy by learning from multiple networks and time-dependent user preferences.
problem Incomplete user profiles and dynamic user preferences degrade recommender quality.
method A cross-network time-aware recommender that learns from multiple source networks and develops current user models.
result The proposed solution achieves superior performance in accuracy, novelty, and diversity.
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…
SM-netFusion estimates brain network atlas by considering multiple topological measures.
problem Limited BNA estimation methods that overlook topological measures and lack discriminative power.
method Supervised multi-topology network cross-diffusion framework using degree, closeness, and eigenvector centrality measures.
result SM-netFusion produces more centered and representative templates, and improves classification accuracy.
LC-GNN improves GNNs for node classification by incorporating label consistency.
problem Limited performance of GNNs due to label consistency assumption not always holding.
method LC-GNN uses node pairs with the same label but unconnected to expand GNN's receptive field.
result LC-GNN outperforms traditional GNNs in semi-supervised node classification.
The cross-domain recommendation technique is an effective way of alleviating the data sparse issue in recommender systems by leveraging the knowledge from relevant domains. Transfer learning is a class of algorithms underlying these techniques. In this paper, we propose a novel transfer learning approach for cross-doma…
A new method improves node classification in graphs with limited labels.
problem Semi-supervised multi-label node classification in attributed graphs.
method Collaborative Graph Walk (Multi-Label-Graph-Walk) using reinforcement learning.
result Significantly better multi-label classification performance compared to state-of-the-art methods.
Proposes GIL for semi-supervised graph classification.
problem Semi-supervised classification of graph data with limited labeled nodes.
method Graph Inference Learning framework that learns node label inference from graph topology.
result Significantly improves semi-supervised node classification performance.
Network node embedding is an active research subfield of complex network analysis. This paper contributes a novel approach to learning network node embeddings and direct node classification using a node ranking scheme coupled with an autoencoder-based neural network architecture. The main advantages of the proposed Dee…
Graph Prototypical Networks improve few-shot node classification on attributed networks.
problem Few-shot node classification in attributed networks with limited labeled instances.
method Graph Prototypical Networks (GPN) using meta-learning to extract meta-knowledge and identify informative labeled instances.
result GPN achieves superior performance in few-shot node classification.
Revises GNN neighborhood aggregation for more accurate node classification.
problem Flaws in benchmark GNN models for node classification.
method Statistical signal processing approach to neighborhood aggregation.
result Novel insights for designing more efficient GNN models.
Graph Posterior Network improves uncertainty estimation for node classification in interdependent graphs.
problem Uncertainty quantification for non-independent node-level predictions in graphs.
method Derives axioms for expected predictive uncertainty, proposes Graph Posterior Network (GPN) which performs Bayesian posterior updates.
result GPN outperforms existing approaches for uncertainty estimation in semi-supervised node classification.
The main approaches for node classification in graphs are information propagation and the association of the class of the node with external information. State of the art methods merge these approaches through Graph Convolutional Networks. We here use the association of topological features of the nodes with their clas…
Boost GNNs for node classification by incorporating label dependencies.
problem Current GNNs lack expressiveness and fail to capture label dependencies.
method Proposes a collective learning framework combining collective classification and self-supervised learning.
result Consistent, significant improvement in node classification accuracy across various GNNs.
Unified model combines GCN and LPA for better node classification.
problem Combining GCN and LPA for improved node classification.
method Unified model that unifies GCN and LPA, learns edge weights and attention weights.
result Unified model outperforms state-of-the-art GCN-based methods in node classification accuracy.
SANNE model generates embeddings for unseen nodes in graph networks.
problem Lack of embeddings for unseen nodes in graph networks.
method SANNE uses a transformer self-attention network to generate embeddings.
result SANNE achieves state-of-the-art results for node classification.
New framework for node classification on graphs using kernel methods.
problem Graph kernel methods for node classification are ill-posed and rely on heuristics.
method Theoretical kernel-based framework for node classification, combining graph kernel methodology with node feature aggregation and data-driven similarity metrics.
result Our framework sets a new state of the art in node classification benchmarks.
New method uses GNNs and node feature propagation for active learning in graph node classification.
problem Lack of labeled data for graph neural networks.
method Node feature propagation followed by K-Medoids clustering for instance selection.
result Proposed method significantly outperforms other methods on benchmark datasets.
This paper investigates the impact of normalization on deep neural networks for click-through rate prediction.
problem The effect of normalization on deep neural network models for CTR estimation.
method Systematic study of various normalization approaches applied to feature embedding and MLP part of DNN models.
result Correct normalization significantly enhances model performance, as demonstrated by extensive experiments on real-world datasets.
This paper improves node classification using graph structure and side information.
problem Improving node classification in semi-supervised scenarios.
method Combines graph convolutional networks with extracted side information.
result The proposed model achieves higher prediction accuracy.
Paper proposes JDR to denoise graph features and rewire graphs for better node classification.
problem Jointly denoise noisy graph features and rewire graphs for improved node classification.
method Align leading spectral spaces of graph and feature matrices to solve non-convex optimization problem.
result JDR consistently outperforms existing methods on various node classification tasks.
Study finds significant instability in node embeddings due to randomness.
problem Stability of node embeddings under random variations.
method Evaluated five node embedding algorithms (HOPE, LINE, node2vec, SDNE, GraphSAGE) on synthetic and empirical graphs.
result Significant instability in embedding spaces and downstream task accuracy.
Paper tackles cybersecurity attack detection with an ensemble approach.
problem Challenges in multi-class classification for cyber security breaches.
method Designing a multi-node multi-class classification ensemble approach.
result Proposed approach outperforms full-data approach in multi-node data-censoring cases.
Graph attention improves node classification by distinguishing important edges.
problem Node classification in graph-based learning models.
method Theoretical analysis of graph attention networks for node classification.
result Graph attention can perfectly classify nodes in an 'easy' regime but fails in a 'hard' regime.
HDGI learns node representations for heterogeneous graphs.
problem Challenges in learning node representations for heterogeneous graphs.
method HDGI uses meta-path structure, graph convolution, and semantic-level attention to maximize local-global mutual information.
result HDGI outperforms state-of-the-art methods on graph-related tasks.
GCNIII combines Wide & Deep for better node classification.
problem Issues with graph convolutional networks in node classification tasks.
method Proposes GCNIII framework integrating Wide & Deep architecture and three techniques.
result Demonstrates improved performance in various node classification tasks.
EPFGNN models graph connections for better node classification.
problem Graph node classification issues due to feature aggregation.
method EPFGNN models graph as a Markov Random Field with explicit pairwise factors and a GNN backbone.
result EPFGNN improves semi-supervised node classification performance.
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.
HyperBERT enhances BERT for node classification on text-attributed hypergraphs.
problem Challenges in capturing hypergraph structure and text attributes in node classification.
method Mixing hypergraph-aware layers with BERT for improved node classification.
result HyperBERT achieves state-of-the-art results on text-attributed hypergraph benchmarks.
HopGAT improves node classification in sparsely labeled graphs by learning from distant neighbors.
problem Classifying nodes in sparsely labeled graphs with limited labeled data.
method Hop-aware supervision mechanism and simulated annealing learning strategy.
result The model achieves high accuracy even with 40% labeled data, reducing performance loss to 3.9%.
Automates graph convolutional network design for semi-supervised node classification.
problem Designing optimal graph convolutional network architectures for semi-supervised node classification.
method An automatic process to define a problem-specific architecture based on graph structure.
result The proposed method outperforms existing methods in classification performance and network compactness.
Proposes a method to quantify uncertainty in graph neural networks for node classification.
problem Uncertainty in graph neural networks for node classification.
method Bayesian uncertainty propagation (BUP) method embedding GNNs in a Bayesian framework.
result Demonstrates superior performance of the proposed method on benchmark datasets.
Improves GNN prediction sets with conformal prediction for node classification.
problem Lack of predictive uncertainty for GNN models.
method Adapted conformal prediction for graph data, weighting scores based on graph structure.
result Better calibrated and tighter prediction sets than naive conformal prediction.
Proposes an alternative approach to propagate labels in GCNs using network diffusion and clustering.
problem Challenges of training GCNs with limited labeled data and bias in network diffusion methods.
method Clustering nodes into communities, using diffusion to quantify proximity, and comparing topological profiles.
result Identifies nodes most similar to labeled nodes, improving label propagation in GCNs.
GraphCL learns node representations by maximizing similarity between perturbed node features.
problem Learning node representations in graph data without labeled data.
method Contrastive learning of node embeddings using graph neural networks and a loss function.
result Significantly outperforms state-of-the-art in unsupervised node classification benchmarks.
Graph embedding is an important approach for graph analysis tasks such as node classification and link prediction. The goal of graph embedding is to find a low dimensional representation of graph nodes that preserves the graph information. Recent methods like Graph Convolutional Network (GCN) try to consider node attri…
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.
PanRep learns universal node embeddings for heterogeneous graphs.
problem Learning universal node embeddings for heterogeneous graphs.
method Graph Neural Network (GNN) model with four decoders capturing different properties.
result PanRep outperforms unsupervised and supervised methods in node classification and link prediction.
SENSE enhances node sequences in graphs using vector embeddings.
problem Efficiently capturing graph node sequences for applications.
method SENSE-S learns node embeddings and composes them for sequences, preserving node order.
result SENSE-S increases multi-label classification and link-prediction accuracy by up to 50% and 78% respectively.
Caps2NE learns node embeddings in graphs using capsule layers.
problem Learning low-dimensional node embeddings in graph data.
method Caps2NE uses a two-capsule layer architecture with a routing process to aggregate neighbors' features.
result Caps2NE achieves state-of-the-art performance on node classification tasks.
Graph convolutional networks (GCNs) have been successfully applied in node classification tasks of network mining. However, most of these models based on neighborhood aggregation are usually shallow and lack the "graph pooling" mechanism, which prevents the model from obtaining adequate global information. In order to …