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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.
- Attentive capsule network for click-through rate and conversion rate prediction in online advertising55
This work proposes a method named Attentive Capsule Network (ACN), which uses Transformers for feature interaction and leverages capsule networks to capture multiple interests from user behavior history and designs a modified dynamic routing algorithm integrating with an attention mechanism.
- Debiased Graph Neural Networks With Agnostic Label Selection Bias54
A novel debiased GNNs (DGNN) with a differentiated decorrelation regularizer is proposed, which outperforms the state-of-the-art methods and DGNN is a flexible framework to enhance existing GNN's.
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- Parallel Societies: A Computing Perspective of Social Digital Twins and Virtual–Real Interactions53
The first issue of the IEEE Transactions on Computational Social Systems (TCSS) of 2020 is released, and the editors, reviewers, authors, and readers are thanked for their great support and effort.
- Constant-Overhead Zero-Knowledge for RAM Programs52
A constant-overhead interactive zero-knowledge (ZK) proof system for RAM programs, that is, a ZK proof in which the communication complexity as well as the running times of the prover and verifier scale linearly in the size of the memory N and the running time T of the underlying RAM program.
- OpenHGNN: An Open Source Toolkit for Heterogeneous Graph Neural Network45
OpenHGNN defines a unified and standard pipeline for training and testing, which can allow users to run a model on a specific dataset with just one command line, and can be extended to meet users' customized needs.
- Can Large Language Models Improve the Adversarial Robustness of Graph Neural Networks?44
An LLM-based robust graph structure inference framework, LLM4RGNN is proposed, which distills the inference capabilities of GPT-4 into a local LLM for identifying malicious edges and an LM-based edge predictor for finding missing important edges, so as to recover a robust graph structure.
- Who You Would Like to Share With? A Study of Share Recommendation in Social E-commerce44
A heterogeneous graph neural network based share recommendation model, called HGSRec is proposed, which delicately designs a tripartite heterogeneous GNNs to describe the multifold characteristics of users and items, and dynamically fuses them via capturing potential ternary dependency with a dual co-attention mechanism.
- Regularizing Graph Neural Networks via Consistency-Diversity Graph Augmentations42
This paper analyzes two representative semi-supervised learning algorithms: label propagation (LP) and consistency regularization (CR) and finds that LP utilizes the prior knowledge of graphs to improve consistency and CR adopts variable augmentations to promote diversity.
- Space4HGNN41
A unified framework covering most HGNNs is proposed, consisting of three components: heterogeneous linear transformation, heterogeneous graph transformation, and heterogeneous message passing layer, and a platform Space4HGNN is built, which offers modularized components, reproducible implementations, and standardized evaluation for HGNN's.
- Motif-Preserving Temporal Network Embedding37
This work proposes MTNE, a novel embedding model for temporal networks that integrates the Hawkes process to stimulate the triad evolution process that preserves motif-aware high-order proximities, but also combines attention mechanism to distinguish the importance of different types of triads better.
- Hierarchical Contrastive Learning Enhanced Heterogeneous Graph Neural Network29
HeCo++ conducts hierarchical contrastive learning, including cross-view and intra-view contrasts, which aims to enhance the mining of respective structures and shows superior performance over the state-of-the-arts.
- Linear Alignment: A Closed-form Solution for Aligning Human Preferences without Tuning and Feedback28
Linear Alignment is introduced, a novel algorithm that aligns language models with human preferences in one single inference step, eliminating the reliance on data annotation and model training.
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- Abnormal Event Detection via Heterogeneous Information Network Embedding22
A novel deep heterogeneous network embedding method which incorporates the entity attributes and second-order structures simultaneously to address the problem of number of co-occurrences of majority entities in events.
- Graph Fairness Learning under Distribution Shifts21
To guarantee fairness performance on unknown testing graphs, a graph generator is proposed to produce numerous graphs with significant bias and under different distributions to achieve high classification and fairness performance even on generated graphs with significant bias, thereby effectively handling unknown testing graphs.
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- Social computing and computational societies: The foundation and consequence of smart societies18
The ultimate goal is to create a novel and configurable social management mode with closed-loop feedback from public opinion, parallel management, and mobile command and control.
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- Decorrelated Clustering with Data Selection Bias15
A novel Decorrelation regularized K-Means algorithm for clustering with data selection bias that aims to learn the global sample weights which are capable of balancing the sample distribution, so as to remove unexpected correlations among features.
- Minimum Topology Attacks for Graph Neural Networks14
An attack model, named MiBTack, based on a dynamic projected gradient descent algorithm, which can effectively solve the involving non-convex constraint optimization on discrete topology and can be used to measure node robustness, which is beyond what the current fixed-budget topology attacks can offer.
- Large-scale Comb-K Recommendation14
The comb-K recommendation model is proposed, a constrained combinatorial optimization model which seamlessly integrates the selection phase and delivery phase with delicately designed constraints and is able to simultaneously search the optimal combination of item selection and delivery with the full consideration of all user preferences.
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- Graph Representation Learning13
A comprehensive overview of a large number of graph representation learning methods in a systematic manner, covering the traditional graph representation learning, modern graph representation learning, and graph neural networks is provided.
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- Iris Image Super Resolution Based on GANs with Adversarial Triplets11
This paper introduces the adversarial idea into the triplet network, and proposes a novel iris image super-resolution architecture with triplet loss, which makes a contribution to enhancing iris images for recognition.
- Prohibited Item Detection via Risk Graph Structure Learning10
RGSL first introduces structure learning into large-scale risk graphs, to reduce noisy connections and add similar pairs, and designs the pairwise training mechanism, which transforms the detection process as a metric learning from candidates to their similar prohibited items.
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- Toward Graph-Tokenizing Large Language Models with Reconstructive Graph Instruction Tuning7
The key idea is to reconstruct the graph information from the LLM's graph token outputs, explicitly incorporating graph supervision to constrain the alignment process, resulting in a reconstructive graph instruction tuning pipeline, RGLM.
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- Federated Continual Novel Class Learning7
A Global Alignment Learning (GAL) framework is proposed that can accurately estimate the global novel class number and provide effective guidance for local training from a global perspective and provide effective guidance for local training from a global perspective, all while maintaining privacy protection.
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- HGOT: Self-supervised Heterogeneous Graph Neural Network with Optimal Transport3
A novel self-supervised Heterogeneous graph neural network with Optimal Transport (HGOT) method which is designed to facilitate self-supervised learning for heterogeneous graphs without graph augmentation strategies and employs the optimal transport mechanism to relieve the laborious sampling process of positive and negative samples.
- Unifying Graph Neural Networks with a Generalized Optimization Framework3
This article establishes a surprising connection between different propagation mechanisms with an optimization problem, and shows that despite the proliferation of various GNNs, their proposed propagation mechanisms are the optimal solutions of a generalized optimization framework with a flexible feature fitting function and a generalized graph regularization term.
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- Platforms and Practice of Heterogeneous Graph Representation Learning2
This chapter introduces OpenHGNN, a toolkit that can help to build HG models in a predesigned pipeline and presents the procedures with three well-known HG models that are HAN, the model that introduces attention mechanism to heterogeneous graph neural networks, RGCN, a model used to model multi-relational graphs with GCN, and HERec, a heterogeneous graph embedding method for recommendation.
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- negMIX: Negative Mixup for OOD Generalization in Open-Set Node Classification1
A novel negative Mixup method purposefully crafted for the open-set scenario with theoretical justification is devised, to enhance the model's generalization to OOD nodes and yield clearer ID/OOD boundary.
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- AuCoGNN: Enhancing Graph Fairness Learning Under Distribution Shifts With Automated Graph Generation1
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- Attribute-Assisted Heterogeneous Graph Representation1
This chapter introduces three attribute-assisted heterogeneous graph representation models including heterogeneous graph attention network (HAN), heterogeneous graph propagation network (HPN), and heterogeneous graph structure learning (HGSL), which simultaneously utilize both complex structural information and rich attribute information to learn node representation.
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- Adaptive Tokenization: On the Hop-Overpriority Problem in Tokenized Graph Learning Models–
The Learnable Graph Token List (LGTL), a plug-and-play module to replace hand-designed token lists in TGLMs, is proposed, which adaptively adjusts the weights across hops and prioritizes informative nodes within hops through a graph attention gate module and a selection module, respectively.
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- Structure-Preserved Heterogeneous Graph Representation–
This chapter introduces several works focusing on structure preservation, which successfully depict the rich semantics and complex heterogeneity, and effectively support downstream tasks ineterogeneous graph.
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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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