Generates missing node attributes for better graph-based tasks.
problem Missing or incomplete node attributes degrade graph-based algorithms' performance.
method Deep adversarial learning-based method (NANG) to generate node attributes.
result Generated node attributes improve node classification and link prediction.
Graph attention auto-encoder reconstructs graph structure and attributes.
problem Lack of methods to reconstruct graph structure and node attributes in graph auto-encoders.
method Stacked encoder/decoder layers with self-attention mechanisms, regularized node representations to reconstruct graph structure.
result Competitive performance on node classification benchmarks, including inductive learning.
WGNN learns graph representations from incomplete attribute data.
problem Missing node attributes in graphs.
method WGNN learns node representations from decomposed attribute matrices and uses Wasserstein space for message passing.
result WGNN outperforms existing methods in node classification tasks with missing attribute data.
AUASE embeds dynamic networks with stability guarantees for node comparison.
problem Stability in dynamic network embeddings for comparing nodes across time.
method Attributed unfolded adjacency spectral embedding (AUASE) for stable unsupervised learning.
result AUASE provides significant improvements in link prediction and node classification.
New method separates graph structure from node attributes to recover lost signal.
problem Standard representation learning on attributed graphs merges incompatible metric spaces, leading to geometrically flawed alignment.
method Custom variational autoencoder that separates manifold learning from structural alignment.
result Transforms geometric conflict into interpretable structural descriptor, uncovering connectivity patterns and anomalies.
DSGC improves graph representation learning by modeling object links and attribute relations.
problem Limited modeling capability of existing GCN variants on noisy and sparse real-world networks.
method Dimensionwise separable 2-D graph convolution (DSGC) that filters node features.
result DSGC achieves significant performance gain over state-of-the-art methods for node classification and clustering.
Proposes VCLANC for attributed network clustering using node and attribute embeddings.
problem Lack of mutual affinity exploitation between nodes and attributes in graph convolution.
method Dual variational auto-encoders for node and attribute embeddings, Gaussian mixture model priors, mutual distance and clustering assignment hardening losses.
result Demonstrates effectiveness on real-world attributed network datasets.
New method learns node features from attributed graphs without node identity constraints.
problem Learning useful node features from attributed graphs for various tasks.
method Inductive representation learning using attributed random walks.
result Generalizes existing methods and supports large graphs.
AnomalyDAE detects anomalies in networks by learning cross-modality interactions.
problem Detecting anomalies in attributed networks where structure and attributes interact.
method Dual autoencoder framework with attention mechanism for joint learning of structure and attribute embeddings.
result AnomalyDAE effectively detects anomalies by reconstructing node attributes and structures.
GraphVRNN generates graphs with latent variables and node attributes.
problem Generating diverse and complex graph structures.
method Probabilistic autoregressive model for graph generation.
result GraphVRNN can model complicated distributions and generate plausible structures and node attributes.
A coloring scheme improves graph neural networks for node disambiguation.
problem Improving graph neural networks' ability to distinguish identical node attributes.
method Introducing a graph neural network called Colored Local Iterative Procedure (CLIP) that uses colors to disambiguate node attributes.
result CLIP is a universal approximator of continuous functions on graphs with node attributes.
New framework for disentangling graph node and edge features.
problem Learning disentangled representations for attributed graphs with node and edge features.
method Proposes a novel variational objective and architecture for node and edge deconvolutions to disentangle latent factors.
result Demonstrates effectiveness of the proposed model and its extensions on synthetic and real-world datasets.
Neural-Brane learns vertex embeddings for networks using both topology and attributes.
problem Lack of nodal attributes in network embedding methods.
method Neural Bayesian Personalized Ranking for Attributed Network Embedding.
result Neural-Brane outperforms state-of-the-art methods in node classification and clustering tasks.
Network embedding leverages the node proximity manifested to learn a low-dimensional node vector representation for each node in the network. The learned embeddings could advance various learning tasks such as node classification, network clustering, and link prediction. Most, if not all, of the existing works, are ove…
GLACE embeds large-scale attributed graphs effectively, preserving structure and attributes.
problem Uncertainty and complexity in large-scale attributed graphs.
method Gaussian embeddings for scalable and efficient graph embedding.
result GLACE outperforms state-of-the-art methods on multiple graph analysis tasks.
SANE scales attribute-aware network embedding with locality.
problem Joint embedding of topology and attributes is challenging due to scalability issues.
method SANE learns joint representations by enforcing local linear relationships between nodes and their K-nearest neighbors in both topology and attribute spaces.
result SANE achieves superior performance and scalability compared to existing methods.
MDNE embeds network structures and attributes for better analysis.
problem Preserving both structural and attribute features in network embedding.
method Multimodal Deep Network Embedding (MDNE) using deep model with multiple layers of non-linear functions.
result MDNE outperforms baselines on various tasks with real-world datasets.
Enhances graph classification by adding virtual nodes to represent latent graph aspects.
problem Graph classification challenges in representing latent graph aspects not directly available from attributes and connectivity.
method Introducing virtual nodes bidirectionally connected to all existing nodes, then using Column Network for representation.
result Virtual Column Network (VCN) outperforms existing methods in bioactivity prediction and vulnerability detection.
Graph2Gauss embeds nodes as Gaussian distributions for versatile graph learning.
problem Efficiently learning node embeddings on large graphs for various tasks.
method Personalized ranking formulation to capture network structure and node attributes.
result Strong performance on link prediction and node classification tasks.
Develops GNNs for incomplete graphs, improving learning from missing node attributes.
problem Learning from incomplete graphs with missing node attributes.
method Introduces PaGNNs with novel partial aggregation functions for incomplete graph data.
result Demonstrates effectiveness and efficiency of PaGNNs on various datasets.
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.
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.
MGCN improves multi-layer graph classification using node attributes and relations.
problem Lack of comprehensive multi-layer graph embedding methods considering node attributes and different types of edges.
method Proposes MGCN, a method that combines GCN for multi-layer graphs, incorporating node attributes and both within and between layer relations.
result MGCN outperforms other multi-layer and single-layer methods in semi-supervised node classification tasks.
F-GCN improves graph convolutional networks for semi-supervised node classification.
problem Improving representation capacity of graph convolutional networks for multi-hop neighborhood information.
method Proposes a mathematically motivated, yet simple extension to existing GCNs.
result F-GCN outperforms state-of-the-art models on six out of eight datasets.
We analyze and generalize graph convolutions for better node attribute representation.
problem Limitations of existing graph convolutional networks (GCNs).
method Proposed a generalization of GCNs with structural properties of local neighborhood graphs and non-weighted aggregation operations.
result The proposed approach is strictly more expressive with a modest increase in parameters and computations.
DMGI embeds multiplex networks with node attributes without supervision.
problem Existing methods fail to handle node attributes and multiple relation types in multiplex networks.
method Inspired by DGI, DMGI maximizes mutual information between local and global graph representations, integrating node embeddings from multiple graphs.
result DMGI outperforms state-of-the-art methods on various downstream tasks.
Consistent spectral clustering with fairness constraints on representation graphs.
problem Finding balanced clusters in similarity graphs with fairness constraints.
method Developed variants of unnormalized and normalized spectral clustering for fair planted partitions.
result Consistency results for constrained spectral clustering under fair planted partitions.
APGE protects graph node representations from inference attacks.
problem Privacy leakage in graph embedding methods.
method Adversarial training framework with disentangling and purging mechanisms.
result APGE preserves structural and utility attributes while concealing private information.
AP-Calculus offers a new framework for causal inference in Bayesian networks.
problem Causal inference in Bayesian networks with complex architectures.
method Introduces Attribution Projection Calculus (AP-Calculus) to determine causal relationships.
result Proves that for each label, exactly one intermediate node acts as a deconfounder.
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.
Unified model generates representations for all nodes in growing graphs.
problem Cold start problem in growing graphs isolates new nodes.
method Generative graph convolutional network that learns adaptive node representations.
result Superior performance on citation network datasets.
Proposes a method to predict node attributes using network topology.
problem Predicting node attributes in graphs for various applications.
method Creates a feature map using all attributes of neighbors to predict attributes values for a node.
result Significantly improves prediction accuracy compared to baseline approaches.
A new method for efficient structural node embeddings using Von Neumann entropy.
problem Efficiently identifying structurally equivalent nodes in complex networks.
method VNEstruct: a simple approach generating low-dimensional structural node embeddings using Von Neumann entropy.
result VNEstruct achieves robustness on structural role identification and state-of-the-art performance on graph classification tasks.
EIGAN learns private representations without centralized data, outperforming state-of-the-art.
problem Private representation learning with multiple ally and adversary attributes.
method Exclusion-Inclusion Generative Adversarial Network (EIGAN) and Distributed EIGAN (D-EIGAN).
result EIGAN and D-EIGAN outperform state-of-the-art methods in accuracy and scalability.
DEAL model predicts links for new nodes with only attribute info.
problem Predicting links for new nodes with only attribute info.
method DEAL model with two encoders and alignment mechanism.
result DEAL significantly outperforms existing methods on inductive link prediction.
AGE improves graph embedding by smoothing features and iteratively enhancing node embeddings.
problem Challenges in attributed graph embedding, especially in preserving optimal low-pass characteristics and robustness.
method AGE, a novel framework combining Laplacian smoothing and adaptive encoding, addresses these issues.
result AGE consistently outperforms state-of-the-art methods on node clustering and link prediction tasks.
Enhances community detection in correlated networks with node attributes.
problem Community detection in multiple networks with correlated node attributes and edges.
method Introduced the correlated Contextual Stochastic Block Model (CSBM), developed a two-step matching procedure.
result Algorithm recovers exact node correspondence, enabling enhanced community detection.
This work tackles community detection in networks with node attributes, achieving exact recovery.
problem Community detection in networks with correlated node attributes.
method Information-theoretic criterion and iterative clustering algorithm maximizing joint likelihood.
result Exact recovery of community labels under a general model for network and node attributes.
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.
Graph InfoClust learns node representations by capturing cluster-level information, improving graph mining tasks.
problem Leveraging cluster-level node information for unsupervised graph representation learning.
method Graph InfoClust (GIC) uses a differentiable K-means method to compute clusters and jointly optimizes mutual information between nodes of the same cluster.
result GIC outperforms state-of-the-art methods in various downstream tasks with a 0.9% to 6.1% gain.
New method combines hypergraph structure and node attributes for better community detection.
problem Improving community detection in hypergraphs with node attributes.
method Developed a principled model that learns from data to combine higher-order interactions and node attributes.
result Strong performance in hyperedge prediction and community detection, especially when attributes are informative.
A new framework generalizes graph-based learning methods, improving transferability and efficiency.
problem Limitations of existing graph-based learning methods, particularly their inability to transfer features to new nodes and graphs.
method Introduced attributed random walks as a basis for generalizing existing methods, enabling broader applicability and transferability.
result Average AUC improvement of 16.1% and significant reduction in space requirements (853 times less) compared to existing methods.
Proposes a novel graph representation learning framework using contrastive methods.
problem Graph representation learning for graph-structured data.
method Leverages a contrastive objective at the node level, generating two graph views by corruption and learning node representations by maximizing agreement.
result Consistently outperforms existing state-of-the-art methods on transductive and inductive learning tasks.
Simple graph representation outperforms complex methods in graph classification.
problem Graph classification and representation learning on graphs.
method Developed a simple yet meaningful graph representation and tested its effectiveness.
result Simple graph representation achieves similar performance to state-of-the-art methods for non-attributed graph classification.
Embed nodes with multi-scale attributes for robust network analysis.
problem Capturing complex node attributes across different scales.
method Multi-scale attributed node embedding (AE & MUSAE) using Skip-gram approach.
result Proves node-feature mutual information is implicitly factorized by embeddings.
FairDrop improves fairness in graph representation learning by counteracting homophily.
problem Ensuring fairness in graph representation learning, especially in scenarios with protected attributes.
method Proposes a biased edge dropout algorithm (FairDrop) to counteract homophily and improve fairness.
result Successfully improves fairness in all models up to a small or negligible drop in accuracy.
SEAL improves AL on attributed graphs by combining deep learning and adversarial strategies.
problem Efficient AL on attributed graphs with label sparsity issues.
method SEAL framework using adversarial components for graph embedding and semi-supervised discriminator.
result Superior performance improvements over state-of-the-art baselines.
Paper defends sensitive attributes in GNNs from inference attacks.
problem Protecting sensitive attributes in GNNs from inference attacks.
method Proposes adversarial training with TV and Wasserstein distance to locally filter sensitive attributes.
result Framework creates strong defense against inference attacks with minimal performance loss.