InceptionGCN improves disease prediction on graph data.
problem Improving disease prediction accuracy on graph data.
method Introduced a new spectral domain architecture with inception modules and varying kernel sizes.
result Significantly improved disease prediction results on two datasets.
This work studies the impact of intra-/inter-class diversity on pre-training datasets and finds a balance for optimal performance.
problem The impact of intra-/inter-class diversity on supervised pre-training datasets and their effect on downstream tasks.
method Empirical study and theoretical analysis of the relationship between diversity types and downstream performance.
result The optimal class-to-sample ratio is invariant to the size of the pre-training dataset and can be predicted.
HGT models heterogeneous graphs with dedicated node and edge representations.
problem Infeasibility of existing GNNs for heterogeneous graphs.
method Node- and edge-type dependent parameters, relative temporal encoding, heterogeneous mini-batch graph sampling.
result HGT outperforms state-of-the-art GNNs by 9%--21% on various downstream tasks.
New method for learning on heterogeneous graphs without meta-paths.
problem Learning on heterogeneous graphs is sensitive to meta-paths choice, leading to poor performance.
method Decompose heterogeneous graph into homogeneous relation-type graphs, combine higher-order representations, use attention mechanisms.
result Our model outperforms state-of-the-art baselines in vertex classification tasks on heterogeneous graph datasets.
Paper introduces HGSL for heterogeneous graphs, improving edge type and weight recovery.
problem Learning structure in heterogeneous graphs with multiple node and edge types.
method Proposes H2MN model for DGPs and derives alternating optimization method.
result Demonstrates superior performance on synthetic and real-world datasets.
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.
GEM detects malicious accounts using adaptive embeddings from heterogeneous graphs.
problem Detecting malicious accounts on a leading mobile payment platform.
method Adaptive learning of discriminative embeddings from heterogeneous account-device graphs with attention mechanism for node importance.
result GEM consistently outperforms competitive methods in detecting malicious accounts.
Graph attention networks improve performance on heterogeneous graphs.
problem Complex performance of GNNs on heterogeneous graphs.
method Integrating positional encodings into graph attention networks.
result Graph attention networks excel in node classification and link prediction.
CoMGNN models heterogeneous graphs with evolving nodes and edges.
problem Modeling complex, evolving graphs with diverse information.
method Meta graph attention on co-evolving heterogeneous graphs.
result Significant improvement over state-of-the-art methods.
HTGCN improves community detection in dynamic, heterogeneous graphs.
problem Challenges in detecting communities in graphs with varying features and temporal dynamics.
method Designs HTGCN combining heterogeneous GCN and residual compressed aggregation for dynamic feature representation.
result HTGCN outperforms existing methods on DBLP and IMDB datasets.
Proposes a method to improve graph neural networks on heterogeneous graphs using meta-paths.
problem Improving graph neural networks on heterogeneous graphs with auxiliary tasks.
method Self-supervised auxiliary learning with meta-paths for heterogeneous graphs.
result Consistently improves link prediction and node classification on heterogeneous graphs.
Proposes HetSANN for learning heterogeneous graph structures without meta-paths.
problem Learning low-dimensional vector space of heterogeneous information networks.
method Implicitly represents heterogeneous information through entity space transformation and attention mechanism.
result Significant improvements over state-of-the-art solutions on public datasets.
The paper introduces heterogeneous manifolds for better graph embeddings.
problem Graph embeddings in Euclidean spaces often fail to capture the curvature of real-world graphs.
method The authors propose heterogeneous rotationally-symmetric manifolds with a radial dimension to account for varying curvature.
result The method improves graph embeddings by better preserving high-order structures and heterogeneous random graphs.
HGP tackles noisy connections and heterogeneity in social graphs for better social recommendation.
problem Challenges in social graphs, especially noisy connections and heterogeneity, hinder GNN-based social recommendation.
method HGP uses a tripartite graph to reduce complexity, personalized PageRank for node embeddings, and attention mechanism for integration.
result HGP outperforms baselines in AUC and F1-score metrics on a large-scale dataset.
Improves decentralized learning by optimizing graph mixing for data heterogeneity.
problem Data heterogeneity impacts convergence in decentralized learning, but existing methods ignore this.
method Characterized and quantified the relationship between graph mixing and data heterogeneity. Proposed an optimization approach to improve convergence.
result Our approach leads to improved test performance across various tasks.
Method for understanding heterogeneous treatment effects in complex causal graphs.
problem Heterogeneity and comorbidity in healthcare problems.
method Developed a new approach to characterize heterogeneous causal effects (HCEs) in graphical contexts, including heterogeneous causal graphs (HCGs) with confounders and mediators.
result Established theoretical forms and properties of HCEs in linear and nonlinear models, and developed interactive structural learning for estimation.
Unified model for multiple healthcare tasks using graph structure.
problem Multiple healthcare tasks with shared graph structure.
method Graph-Convolutional Network (GCN) with multiple variational autoencoders.
result Boosted performance across all tasks, outperforming state-of-the-art.
Proposes BGNN for tumor heterogeneity prediction using graph neural networks.
problem Tumor classification limitations and heterogeneity assessment challenges.
method Artificial data generation, tumor heterogeneity estimation, and BGNN model development.
result BGNN achieves 89.67% accuracy in predicting tumor heterogeneity. Bi-directional Curriculum Learning improves graph anomaly detection by considering both homogeneity and heterogeneity.
problem Existing graph anomaly detection methods often ignore the different contributions of nodes to training.
method Introduces Bi-directional Curriculum Learning (BCL) to optimize GAD methods by considering both homogeneity and heterogeneity of nodes.
result Extensive experiments show that BCL significantly improves the performance of GAD anomaly detection models.
New GNN method detects money laundering in diverse customer relationships.
problem Insufficient precision and efficiency of current AML systems.
method Heterogeneous Graph Neural Network (GNN) approach.
result Great potential for enhancing electronic surveillance systems for money laundering.
DeepHGNN detects program identity changes using system behaviors.
problem Detecting program identity changes to prevent malware.
method Attentional heterogeneous graph neural network (DeepHGNN) for graph classification.
result DeepHGNN effectively identifies program identities across various metrics.
Improved community detection in sparse graphs using Bethe-Hessian matrix.
problem Community detection in sparse heterogeneous graphs.
method Spectral clustering based on the Bethe-Hessian matrix Hr for degree-corrected stochastic block models. result Clustering is insensitive to degree heterogeneity for r=ζ. GDA-HIN adapts across heterogeneous networks by aligning shared and private node types.
problem Domain adaptation challenges in heterogeneous networks with shared and private node types.
method Generalized Domain Adaptive model across HINs (GDA-HIN) that aligns identical-type nodes and edges while utilizing different-type nodes and edges.
result GDA-HIN outperforms state-of-the-art methods in various domain adaptation tasks across heterogeneous networks.
AEGCN uses autoencoder constraints to improve graph node classification.
problem Node classification on graph domains with reduced information loss.
method Autoencoder-constrained graph convolutional network (AEGCN).
result Adding autoencoder constraints significantly improves graph convolutional network performance.
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.
Polynomial-time test for detecting dense subgraphs in heterogeneous networks.
problem Detecting a planted community in heterogeneous networks.
method Proposes a polynomial-time test with a standard normal distribution null limiting distribution.
result The test is efficient and performs well in both simulations and real data.
Study shows neural ODEs generalize well on synthetic graphs but struggle with degree heterogeneity and clustering.
problem Understanding neural ODEs on complex networks, especially with varying graph sizes and structures.
method Synthetic data from five dynamical systems on graphs, using Barabási-Barzel form vector fields.
result Degree heterogeneity and dynamical system type are primary factors affecting neural ODEs' generalization.
A method detects changes in heterogeneous data streams over graph nodes.
problem Detecting changes in data streams from nodes of a graph.
method Online non-parametric method using likelihood-ratio estimation.
result The method accurately identifies change-points in real-world applications.
Develops a model to detect shared communities in non-aligned graphs.
problem Clustering and community detection in non-aligned graphs with heterogeneous populations.
method Joint Stochastic Blockmodel (Joint SBM) and efficient spectral clustering.
result The joint model better estimates communities compared to separate SBMs on individual graphs.
Graph neural networks improve topology control of power grids.
problem Grid congestion due to renewable energy and electrification.
method Investigated the effect of graph representation on GNN effectiveness for topology control.
result Heterogeneous graph representation outperforms homogeneous in topology control tasks.
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.
MatchGNet detects malware by learning program behavior graphs.
problem Malware evasion through obfuscation and high false positives in traditional detection methods.
method Heterogeneous Graph Matching Network model that learns graph representation and similarity metrics.
result MatchGNet reduces false positives by 50% while maintaining zero false negatives.
SStaGCN improves GCN by stacking and aggregation for better node feature extraction.
problem Mitigating over-smoothing in GCN for heterogeneous graph data.
method SStaGCN combines stacking and aggregation to improve GCN performance.
result SStaGCN effectively mitigates over-smoothing and enhances node feature extraction.
PINE embeds graph nodes flexibly, capturing any neighbor dependency.
problem Learning flexible node representations from graph neighborhoods.
method PINE uses partial permutation invariant set functions to capture any possible neighbor dependencies.
result PINE outperforms state-of-the-art methods on various graph learning tasks.
GHNet improves graph learning by balancing homogeneity and heterogeneity.
problem Over-smoothing in GCN leads to similar node representations.
method GHNet uses gating units to balance homogeneity and heterogeneity in feature propagation.
result GHNet achieves larger receptive fields without over-smoothing.
MEGA and MEGA++ embed meta-paths and meta-graphs for better network similarity.
problem Ignoring meta-paths in meta-graph-based relevance computing.
method MEGA++ uses tensor decomposition and coupled tensor-matrix decomposition for joint node embedding.
result MEGA and MEGA++ outperform state-of-the-art approaches in similarity search.
This work identifies and mitigates topological bias in HGNNs using meta-weighting and debiasing.
problem Topological bias in HGNNs affects model performance on specific nodes.
method Meta-weighting adjacency matrix, PageRank projection, debiasing structure.
result The debiasing structure improves HGNNs' performance and debiasing.
Proposes a new method to optimize graph neural network architectures on heterogeneous information networks.
problem Weaknesses in instability and inflexibility of existing graph neural architecture search methods.
method Partial Message Meta Multigraph search (PMMM) using a differentiable framework to search for a meaningful meta multigraph.
result Significantly more stable and effective than state-of-the-art heterogeneous GNNs.
Friend recommendation system using heterogeneous edge embeddings.
problem Inadequate performance of existing network embedding techniques on multi-graph social networks.
method Proposes a method to mine network representation exploiting heterogeneity in multi-graphs.
result Outperforms state-of-the-art baselines on Hike's social network in terms of accuracy and user satisfaction.
We develop meta-path embeddings to improve feature learning in heterogeneous knowledge graphs.
problem Redundant and unsuitable categorical features in meta-paths for machine learning models.
method Skipgram model with meta-path extension for learning semantical and compact vector representations.
result Meta-path embeddings improve link prediction on Wikidata.
THGFM models dynamic relational systems with cross-type and temporal fusion.
problem Learning on temporal heterogeneous graphs with diverse node and relation types.
method Dual-Path Architecture with Shared-Space and Relational Type-Partitioned Temporal Attention.
result THGFM outperforms baseline models on academic graph benchmarks.
HeteGCN improves text classification with efficient, scalable graph models.
problem Text classification with large datasets and features, especially in small labeled sets.
method HeteGCN combines PTE and TextGCN, using heterogeneous graphs and feature embeddings.
result HeteGCN achieves better performance and scalability compared to existing methods.
ie-HGCN addresses HIN challenges by efficiently learning node representations.
problem Lack of flexibility in exploring meta-paths and high computational complexity in HIN GCN methods.
method Hierarchical aggregation architecture that automatically extracts useful meta-paths and reduces computational cost.
result ie-HGCN outperforms state-of-the-art methods on real network datasets.
Paper proposes HGAT for detecting fake news on HIN.
problem Viral spread of fake news causing social harm.
method Hierarchical Graph Attention Network (HGAT) for node representation learning in HIN.
result HGAT outperforms text-based and network-based models.
Proposes a hybrid model for stock market report classification using graph neural networks.
problem Lack of unified node embeddings for heterogeneous graphs in text datasets.
method Transductive hybrid approach combining unsupervised node representation learning and supervised node classification/edge prediction.
result Demonstrates the model's ability to classify stock market technical analysis reports.
This research tackles sample complexity in causal graph recovery with temporal heterogeneity.
problem Recovering a unique causal graph from observational data with temporal heterogeneity.
method Integrates time-series dynamics and multi-environment heterogeneity to constrain the problem, enabling a rigorous analysis of statistical limits.
result Unified necessary identifiability conditions and explicit information-theoretic bounds quantify the sample complexity under different noise distributions.
Proposes a THGNN for dynamic financial time series prediction.
problem Challenges in predicting stock market price movements.
method Temporal and heterogeneous graph neural network (THGNN) approach.
result Significantly improved prediction performance compared to state-of-the-art methods.
TF-GNN simplifies graph neural networks in TensorFlow.
problem Handling rich heterogeneous graph data in machine learning.
method A scalable library with a Keras message passing API.
result Enables low-code solutions for broader developers.