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- A Survey on Network Embedding1,269
This survey focuses on categorizing and then reviewing the current development on network embedding methods, and point out its future research directions, covering the structure- and property-preserving network embeding methods, the network embedded methods with side information, and the advanced information preserving network embedting methods.
- Community Preserving Network Embedding935
A novel Modularized Nonnegative Matrix Factorization (M-NMF) model is proposed to incorporate the community structure into network embedding and jointly optimize NMF based representation learning model and modularity based community detection model in a unified framework, which enables the learned representations of nodes to preserve both of the microscopic and community structures.
- 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.
- 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.
- 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.
- 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.
- 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.
- 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.
- Network Schema Preserving Heterogeneous Information Network Embedding152
This paper makes the first attempt to study network schema preserving HIN embedding, and proposes a novel model named NSHE, which significantly outperforms the state-of-the-art methods.
- Graph Structure Estimation Neural Networks151
The proposed graph estimation neural networks GEN presents a structure model to fit the mechanism of GNNs by generating graphs with community structure, and an observation model that injects multifaceted observations into calculating the posterior distribution of graphs and is the first to incorporate multi-order neighborhood information.
- Self-supervised Graph Neural Networks for Multi-behavior Recommendation142
This paper proposes a novel self-supervised graph collaborative filtering model for multi-behavior recommendation named S-MBRec, and proposes a star-style contrastive learning task to capture the embedding commonality between target and auxiliary behaviors, so as to alleviate the sparsity of supervision signal, reduce the redundancy among auxiliary behavior, and extract the most critical information.
- CuCo: Graph Representation with Curriculum Contrastive Learning141
This paper introduces four graph augmentation techniques to obtain the positive and negative samples, and utilize graph neural networks to learn their representations, and proposes a novel curriculum contrastive learning framework for self-supervised graph-level representation, called CuCo.
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- Multi-Component Graph Convolutional Collaborative Filtering130
This paper proposes a novel Multi-Component graph convolutional Collaborative Filtering (MCCF) approach to distinguish the latent purchasing motivations underneath the observed explicit user-item interactions, and empirical results on three real datasets and a synthetic dataset show the significant performance gains of MCCF.
- Lorentzian Graph Convolutional Networks129
This paper rebuilds the graph operations of hyperbolic GCNs with Lorentzian version, e.g., the feature transformation and non-linear activation, and proves some proposed graph operations are equivalent in different types ofhyperbolic geometry, which fundamentally indicates their correctness.
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- Independence Promoted Graph Disentangled Networks120
This paper firstly presents disentangled representation learning by neighborhood routing mechanism, and then employs the Hilbert-Schmidt Independence Criterion to enforce independence between the latent representations, which is effectively integrated into a graph convolutional framework as a regularizer at the output layer.
- Deep Collaborative Filtering with Multi-Aspect Information in Heterogeneous Networks106
A Neural network based Aspect-level Collaborative Filtering model (NeuACF) to exploit different aspect latent factors and NeuACf++ to fuse aspect-level latent factors with self-attention mechanism are proposed.
- Can Small Language Models be Good Reasoners for Sequential Recommendation?88
A novel Step-by-step knowLedge knowLedge fraMework for recommendation (SLIM) is proposed, paving a promising path for sequential recommenders to enjoy the exceptional reasoning capabilities of LLMs in a "slim" (i.e. resource-efficient) manner.
- EasyJailbreak: A Unified Framework for Jailbreaking Large Language Models73
This paper introduces EasyJailbreak, a unified framework simplifying the construction and evaluation of jailbreak attacks against LLMs, which supports 11 distinct jailbreak methods and facilitates the security validation of a broad spectrum of LLMs.
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- Robust Heterogeneous Graph Neural Networks against Adversarial Attacks64
This work proposes a novel robust HGNN framework RoHe against topology adversarial attacks by equipping an attention purifier, which can prune malicious neighbors based on topology and feature, and introduces the metapath-based transiting probability as the prior criterion of the purifier.
- Geometric Disentangled Collaborative Filtering63
A novel generative GDCF model is proposed to learn geometric disentangled representations by inferring the high-level concepts associated with user intentions and various geometries, and the experimental results demonstrate the superiority of this model.
- Hierarchical Graph Interaction Transformer With Dynamic Token Clustering for Camouflaged Object Detection58
A hierarchical graph interaction network termed HGINet is proposed, which is capable of discovering imperceptible objects via effective graph interaction among the hierarchical tokenized features via effective graph interaction among the hierarchical tokenized features.
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 2026-10-16. 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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