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- Zhongzhi ShiSuggested from co-authorship
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Works360 from public data
- Heterogeneous Graph Attention Network3,463
Extensive experimental results on three real-world heterogeneous graphs not only show the superior performance of the proposed model over the state-of-the-arts, but also demonstrate its potentially good interpretability for graph analysis.
- A survey of heterogeneous information network analysis1,112
A survey of heterogeneous information network analysis is provided, which introduces basic concepts of heterogeneous information network analysis, examines its developments on different data mining tasks, discusses some advanced topics, and point out some future research directions.
- Beyond Low-frequency Information in Graph Convolutional Networks868
An experimental investigation assessing the roles of low-frequency and high-frequency signals is presented, and a novel Frequency Adaptation Graph Convolutional Networks (FAGCN) with a self-gating mechanism is proposed, which can adaptively integrate different signals in the process of message passing.
- Leveraging Meta-path based Context for Top- N Recommendation with A Neural Co-Attention Model716
A novel deep neural network with the co-attention mechanism for leveraging rich meta-path based context for top-N recommendation and performs well in the cold-start scenario and has potentially good interpretability for the recommendation results.
- Self-supervised Heterogeneous Graph Neural Network with Co-contrastive Learning572
This paper proposes a novel co-contrastive learning mechanism for HGNNs, named HeCo, which differs from traditional contrastive learning which only focuses on contrasting positive and negative samples, and employs cross-view contrastive mechanism.
- A Survey on Heterogeneous Graph Embedding: Methods, Techniques, Applications and Sources480
This survey presents several widely deployed systems that have demonstrated the success of HG embedding techniques in resolving real-world application problems with broader impacts and summarizes the open-source code, existing graph learning platforms and benchmark datasets.
- Metapath-guided Heterogeneous Graph Neural Network for Intent Recommendation394
A metapath-guided heterogeneous Graph Neural Network to learn the embeddings of objects in intent recommendation as a Heterogeneous Information Network is proposed and Offline experiments on real large-scale data show the superior performance of the proposed MEIRec, compared to representative methods.
- Heterogeneous Graph Attention Networks for Semi-supervised Short Text Classification367
A novel heterogeneous graph neural network based method for semi-supervised short text classification, leveraging full advantage of few labeled data and large unlabeled data through information propagation along the graph is proposed.
- HeteSim: A General Framework for Relevance Measure in Heterogeneous Networks350
Empirical studies show that HeteSim can effectively and efficiently evaluate the relatedness of heterogeneous objects, which is crucial to many data mining tasks.
- Heterogeneous Graph Structure Learning for Graph Neural Networks334
This work makes the first attempt towards learning an optimal heterogeneous graph structure for HGNNs and proposes a novel framework HGSL, which jointly performs Heterogeneous Graph Structure Learning and GNN parameters learning for classification task.
- Meta-learning on Heterogeneous Information Networks for Cold-start Recommendation313
This work proposes a novel semantic-enhanced tasks constructor and a co-adaptation meta-learner to address the two questions for how to capture HIN-based semantics in the meta-learning setting, and how to learn the general knowledge that can be easily adapted to multifaceted semantics.
- Semantic Path based Personalized Recommendation on Weighted Heterogeneous Information Networks290
This paper is the first to propose the weighted HIN and weighted meta path concepts to subtly depict the path semantics through distinguishing different link attribute values, and proposes a semantic path based personalized recommendation method SemRec to predict the rating scores of users on items.
- Compare to The Knowledge: Graph Neural Fake News Detection with External Knowledge266
A novel end-to-end graph neural model called CompareNet is proposed, which compares the news to the knowledge base (KB) through entities for fake news detection and significantly outperforms state-of-the-art methods.
- One2Multi Graph Autoencoder for Multi-view Graph Clustering264
This paper makes the first attempt to employ deep learning technique for attributed multi-view graph clustering, and proposes a novel task-guided One2Multi graph autoencoder clustering framework that can jointly optimize the cluster label assignments and embeddings suitable forgraph clustering.
- Interpreting and Unifying Graph Neural Networks with An Optimization Framework244
A surprising connection is established between different propagation mechanisms with a unified optimization problem, showing that despite the proliferation of various GNNs, in fact, their proposed propagation mechanisms are the optimal solution optimizing a feature fitting function over a wide class of graph kernels with a graph regularization term.
- Adversarial Learning on Heterogeneous Information Networks231
A novel framework HeGAN for HIN embedding is developed, which trains both a discriminator and a generator in a minimax game and proposes a generalized generator, which samples "latent" nodes directly from a continuous distribution, not confined to the nodes in the original network as existing methods are.
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- HGAT: Heterogeneous Graph Attention Networks for Semi-supervised Short Text Classification203
A novel heterogeneous graph neural network-based method for semi-supervised short text classification, leveraging full advantage of limited labeled data and large unlabeled data through information propagation along the graph.
- Hyperbolic Graph Attention Network185
This article exploits the graph attention network to learn robust node representations of graphs in hyperbolic spaces as the gyrovector space framework provides an elegant algebraic formalism forHyperbolic geometry, and utilizes this framework to learn the graph representations in hyperBolic spaces.
- Multi-objective community detection in complex networks185
It is demonstrated that a combination of two negatively correlated objectives under the multi-objective framework usually leads to remarkably better performance compared with either of the orignal single objectives, including even many popular algorithms.
- Specformer: Spectral Graph Neural Networks Meet Transformers176
This work introduces Specformer, which effectively encodes the set of all eigenvalues and performs self-attention in the spectral domain, leading to a learnable set-to-set spectral filter and design a decoder with learnable bases to enable non-local graph convolution.
- Learning to Pre-train Graph Neural Networks171
L2P-GNN is proposed, a self-supervised pre-training strategy for GNNs that attempts to learn how to fine-tune during the pre- training process in the form of transferable prior knowledge.
- Extract the Knowledge of Graph Neural Networks and Go Beyond it: An Effective Knowledge Distillation Framework170
This paper proposes a framework based on knowledge distillation that extracts the knowledge of an arbitrary learned GNN model (teacher model), and injects it into a well-designed student model, which is designed as a trainable combination of parameterized label propagation and feature transformation modules.
- Cash-Out User Detection Based on Attributed Heterogeneous Information Network with a Hierarchical Attention Mechanism158
This work proposes a novel hierarchical attention mechanism based cash-out user detection model, called HACUD, which enhances feature representation of objects through meta-path based neighbors exploiting different aspects of structure information in AHIN.
- Generalizing Graph Neural Networks on Out-of-Distribution Graphs154
This paper argues that the spurious correlation exists among subgraph-level units and analyzes the degeneration of GNN in causal view, and proposes a general causal representation framework for stable GNN, called StableGNN, which not only outperforms the state-of-the-arts but also provides a flexible framework to enhance existing GNNs.
Publication data from OpenAlex, with missing venues and authors filled in from Crossref; citation counts are the higher of OpenAlex and Semantic Scholar, last synced 2026-10-15. One-sentence summaries under some papers are written by Semantic Scholar’s model. Citation counts may be lower than on Google Scholar, which indexes more sources.
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