Papers Heterogeneous Node Classification
“Heterogeneous Node Classification” 태그가 달린 논문 17편 · 필터 해제
HeteroHBA: A Generative Structure-Manipulating Backdoor Attack on Heterogeneous Graphs
Heterogeneous graph neural networks (HGNNs) have achieved strong performance in many real-world applications, yet targeted backdoor poisoning on heterogeneous graphs remains less studied. We consider backdoor attacks for…
Heterogeneous Node ClassificationGraph LearningFrom Primes to Paths: Enabling Fast Multi-Relational Graph Analysis
Multi-relational networks capture intricate relationships in data and have diverse applications across fields such as biomedical, financial, and social sciences. As networks derived from increasingly large datasets becom…
Graph RegressionHeterogeneous Node ClassificationNode ClassificationRelation PredictionSlotGAT: Slot-based Message Passing for Heterogeneous Graph Neural Network
Heterogeneous graphs are ubiquitous to model complex data. There are urgent needs on powerful heterogeneous graph neural networks to effectively support important applications. We identify a potential semantic mixing iss…
Graph Neural NetworkHeterogeneous Node ClassificationLink PredictionNode ClassificationEfficient Heterogeneous Graph Learning via Random Projection
Heterogeneous Graph Neural Networks (HGNNs) are powerful tools for deep learning on heterogeneous graphs. Typical HGNNs require repetitive message passing during training, limiting efficiency for large-scale real-world g…
Graph LearningGraph Neural NetworkHeterogeneous Node ClassificationNode Property PredictionSimple and Efficient Heterogeneous Graph Neural Network
Heterogeneous graph neural networks (HGNNs) have powerful capability to embed rich structural and semantic information of a heterogeneous graph into node representations. Existing HGNNs inherit many mechanisms from graph…
Graph Neural NetworkHeterogeneous Node ClassificationNode Property PredictionAre we really making much progress? Revisiting, benchmarking, and refining heterogeneous graph neural networks
Heterogeneous graph neural networks (HGNNs) have been blossoming in recent years, but the unique data processing and evaluation setups used by each work obstruct a full understanding of their advancements. In this work, …
BenchmarkingHeterogeneous Node ClassificationScalable Graph Neural Networks for Heterogeneous Graphs
Graph neural networks (GNNs) are a popular class of parametric model for learning over graph-structured data. Recent work has argued that GNNs primarily use the graph for feature smoothing, and have shown competitive res…
Heterogeneous Node ClassificationNode Property PredictionHeterogeneous Graph Transformer
Recent years have witnessed the emerging success of graph neural networks (GNNs) for modeling structured data. However, most GNNs are designed for homogeneous graphs, in which all nodes and edges belong to the same types…
Graph SamplingHeterogeneous Node ClassificationNode Property PredictionAn Attention-based Graph Neural Network for Heterogeneous Structural Learning
In this paper, we focus on graph representation learning of heterogeneous information network (HIN), in which various types of vertices are connected by various types of relations. Most of the existing methods conducted …
Graph EmbeddingGraph Neural NetworkGraph Representation LearningHeterogeneous Node Classification+3Non-local Attention Learning on Large Heterogeneous Information Networks
Heterogeneous information network (HIN) summarizes rich structural information in real-world datasets and plays an important role in many big data applications. Recently, graph neural networks have been extended to the r…
Heterogeneous Node ClassificationRepresentation LearningHeterogeneous Deep Graph Infomax
Graph representation learning is to learn universal node representations that preserve both node attributes and structural information. The derived node representations can be used to serve various downstream tasks, such…
ClassificationClusteringGeneral ClassificationGraph Neural Network+5Graph Transformer Networks
Graph neural networks (GNNs) have been widely used in representation learning on graphs and achieved state-of-the-art performance in tasks such as node classification and link prediction. However, most existing GNNs are …
General ClassificationHeterogeneous Node ClassificationLink PredictionNode Classification+1Heterogeneous Graph Attention Networks for Semi-supervised Short Text Classification
Short text classification has found rich and critical applications in news and tweet tagging to help users find relevant information. Due to lack of labeled training data in many practical use cases, there is a pressing …
ClassificationGeneral ClassificationGraph AttentionGraph Neural Network+3Multi-Relational Classification via Bayesian Ranked Non-Linear Embeddings
The task of classifying multi-relational data spans a wide range of domains such as document classification in citation networks, classification of emails, and protein labeling in proteins interaction graphs. Current sta…
ClassificationDocument ClassificationGeneral ClassificationHeterogeneous Node Classification+2Graph Attention Networks
We present graph attention networks (GATs), novel neural network architectures that operate on graph-structured data, leveraging masked self-attentional layers to address the shortcomings of prior methods based on graph …
Document ClassificationGraph AttentionGraph ClassificationGraph Embedding+10Modeling Relational Data with Graph Convolutional Networks
Knowledge graphs enable a wide variety of applications, including question answering and information retrieval. Despite the great effort invested in their creation and maintenance, even the largest (e.g., Yago, DBPedia o…
DecoderGeneral ClassificationGraph ClassificationHeterogeneous Node Classification+7Semi-Supervised Classification with Graph Convolutional Networks
We present a scalable approach for semi-supervised learning on graph-structured data that is based on an efficient variant of convolutional neural networks which operate directly on graphs. We motivate the choice of our …
Document ClassificationDrug DiscoveryGeneral ClassificationGraph Classification+8