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Found 63 papers, 31 papers with code
FairSIN: Achieving Fairness in Graph Neural Networks through Sensitive Information Neutralization
The F3 are expected to statistically neutralize the sensitive bias in node representations and provide additional nonsensitive information.
BurstAttention: An Efficient Distributed Attention Framework for Extremely Long Sequences
Effective attention modules have played a crucial role in the success of Transformer-based large language models (LLMs), but the quadratic time and memory complexities of these attention modules also pose a challenge when processing long sequences.
Learning Invariant Representations of Graph Neural Networks via Cluster Generalization
To address this challenge, we propose the Cluster Information Transfer (CIT) mechanism (Code available at https://github. com/BUPT-GAMMA/CITGNN), which can learn invariant representations for GNNs, thereby improving their generalization ability to various and unknown test graphs with structure shift.
Minimum Topology Attacks for Graph Neural Networks
To break this dilemma, we propose a new type of topology attack, named minimum-budget topology attack, aiming to adaptively find the minimum perturbation sufficient for a successful attack on each node.
GraphTranslator: Aligning Graph Model to Large Language Model for Open-ended Tasks
Large language models (LLMs) like ChatGPT, exhibit powerful zero-shot and instruction-following capabilities, have catalyzed a revolutionary transformation across diverse fields, especially for open-ended tasks.
Graph Fairness Learning under Distribution Shifts
Recently, there has been an increasing interest in ensuring fairness on GNNs, but all of them are under the assumption that the training and testing data are under the same distribution, i. e., training data and testing data are from the same graph.
Graph Contrastive Invariant Learning from the Causal Perspective
How can we fix it and encourage the current GCL to learn better invariant representations?
Graph Invariant Learning with Subgraph Co-mixup for Out-Of-Distribution Generalization
In this paper, we propose a novel graph invariant learning method based on invariant and variant patterns co-mixup strategy, which is capable of jointly generating mixed multiple environments and capturing invariant patterns from the mixed graph data.
Graph Representation Learning
Out-of-Distribution Generalization
A Generalized Neural Diffusion Framework on Graphs
In this paper, we propose a general diffusion equation framework with the fidelity term, which formally establishes the relationship between the diffusion process with more GNNs.
Which Matters Most in Making Fund Investment Decisions? A Multi-granularity Graph Disentangled Learning Framework
In this paper, we highlight that both conformity and risk preference matter in making fund investment decisions beyond personal interest and seek to jointly characterize these aspects in a disentangled manner.
Federated Heterogeneous Graph Neural Network for Privacy-preserving Recommendation
Existing HIN-based recommender systems operate under the assumption of centralized storage and model training.
Towards Graph Foundation Models: A Survey and Beyond
Foundation models have emerged as critical components in a variety of artificial intelligence applications, and showcase significant success in natural language processing and several other domains.
Graph Condensation via Eigenbasis Matching
The increasing amount of graph data places requirements on the efficiency and scalability of graph neural networks (GNNs), despite their effectiveness in various graph-related applications.
Data-centric Graph Learning: A Survey
Recently, instead of designing more complex neural architectures as model-centric approaches, the attention of AI community has shifted to data-centric ones, which focuses on better processing data to strengthen the ability of neural models.
Provable Training for Graph Contrastive Learning
To this end, we propose the PrOvable Training (POT) for GCL, which regularizes the training of GCL to encode node embeddings that follows the GCL principle better.
Hierarchical Contrastive Learning Enhanced Heterogeneous Graph Neural Network
Specifically, two views of a HIN (network schema and meta-path views) are proposed to learn node embeddings, so as to capture both of local and high-order structures simultaneously.
Graph Mining for Cybersecurity: A Survey
In recent years, with the proliferation of graph mining techniques, many researchers investigated these techniques for capturing correlations between cyber entities and achieving high performance.
Abnormal Event Detection via Hypergraph Contrastive Learning
AEHCL designs the intra-event and inter-event contrastive modules to exploit self-supervised AHIN information.
Specformer: Spectral Graph Neural Networks Meet Transformers
To tackle these issues, we introduce 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.
A Survey on Spectral Graph Neural Networks
Graph neural networks (GNNs) have attracted considerable attention from the research community.
MA-GCL: Model Augmentation Tricks for Graph Contrastive Learning
We argue that typical data augmentation techniques (e. g., edge dropping) in GCL cannot generate diverse enough contrastive views to filter out noises.
xTrimoABFold: De novo Antibody Structure Prediction without MSA
Its improvement in both accuracy and efficiency makes it a valuable tool for de novo antibody design and could make further improvements in immuno-theory.
Directed Acyclic Graph Structure Learning from Dynamic Graphs
In a dynamic graph, we propose to simultaneously estimate contemporaneous relationships and time-lagged interaction relationships between the node features.
Uncovering the Structural Fairness in Graph Contrastive Learning
Recent studies show that graph convolutional network (GCN) often performs worse for low-degree nodes, exhibiting the so-called structural unfairness for graphs with long-tailed degree distributions prevalent in the real world.
Revisiting Graph Contrastive Learning from the Perspective of Graph Spectrum
Then we theoretically prove that GCL is able to learn the invariance information by contrastive invariance theorem, together with our GAME rule, for the first time, we uncover that the learned representations by GCL essentially encode the low-frequency information, which explains why GCL works.
Debiasing Graph Neural Networks via Learning Disentangled Causal Substructure
However, by presenting a graph classification investigation on the training graphs with severe bias, surprisingly, we discover that GNNs always tend to explore the spurious correlations to make decision, even if the causal correlation always exists.
KGNN: Distributed Framework for Graph Neural Knowledge Representation
Knowledge representation learning has been commonly adopted to incorporate knowledge graph (KG) into various online services.
Data-Free Adversarial Knowledge Distillation for Graph Neural Networks
Graph neural networks (GNNs) have been widely used in modeling graph structured data, owing to its impressive performance in a wide range of practical applications.
An Effective Graph Learning based Approach for Temporal Link Prediction: The First Place of WSDM Cup 2022
Temporal link prediction, as one of the most crucial work in temporal graphs, has attracted lots of attention from the research area.
Space4HGNN: A Novel, Modularized and Reproducible Platform to Evaluate Heterogeneous Graph Neural Network
Heterogeneous Graph Neural Network (HGNN) has been successfully employed in various tasks, but we cannot accurately know the importance of different design dimensions of HGNNs due to diverse architectures and applied scenarios.
Confidence May Cheat: Self-Training on Graph Neural Networks under Distribution Shift
To this end, in this paper, we propose a novel Distribution Recovered Graph Self-Training framework (DR-GST), which could recover the distribution of the original labeled dataset.
Debiased Graph Neural Networks with Agnostic Label Selection Bias
Then to remove the bias in GNN estimation, we propose a novel Debiased Graph Neural Networks (DGNN) with a differentiated decorrelation regularizer.
Compact Graph Structure Learning via Mutual Information Compression
Furthermore, we maintain the performance of estimated views and the final view and reduce the mutual information of every two views.
Generalizing Graph Neural Networks on Out-Of-Distribution Graphs
Graph Neural Networks (GNNs) are proposed without considering the agnostic distribution shifts between training and testing graphs, inducing the degeneration of the generalization ability of GNNs on Out-Of-Distribution (OOD) settings.
Be Confident! Towards Trustworthy Graph Neural Networks via Confidence Calibration
Specifically, we first verify that the confidence distribution in a graph has homophily property, and this finding inspires us to design a calibration GNN model (CaGCN) to learn the calibration function.
Compare to The Knowledge: Graph Neural Fake News Detection with External Knowledge
Specifically, we first construct a \textit{directed heterogeneous document graph} for each news incorporating topics and entities.
Self-supervised Heterogeneous Graph Neural Network with Co-contrastive Learning
Then the cross-view contrastive learning, as well as a view mask mechanism, is proposed, which is able to extract the positive and negative embeddings from two views.
Lorentzian Graph Convolutional Networks
We also find that the performance of some hyperbolic GCNs can be improved by simply replacing the graph operations with those we defined in this paper.
Extract the Knowledge of Graph Neural Networks and Go Beyond it: An Effective Knowledge Distillation Framework
Our framework extracts the knowledge of an arbitrary learned GNN model (teacher model), and injects it into a well-designed student model.
Ranked #1 on
Node Classification
on Cora (0.5%)
Interpreting and Unifying Graph Neural Networks with An Optimization Framework
Graph Neural Networks (GNNs) have received considerable attention on graph-structured data learning for a wide variety of tasks.
Beyond Low-frequency Information in Graph Convolutional Networks
For a deeper understanding, we theoretically analyze the roles of low-frequency signals and high-frequency signals on learning node representations, which further explains why FAGCN can perform well on different types of networks.
Learning Discrete Adaptive Receptive Fields for Graph Convolutional Networks
To address this weakness, we introduce a novel framework of conducting graph convolutions, where nodes are discretely selected among multi-hop neighborhoods to construct adaptive receptive fields (ARFs).
Learning Node Representations from Noisy Graph Structures
Since noises are often unknown on real graphs, we design two generators, namely a graph generator and a noise generator, to identify normal structures and noises in an unsupervised setting.
A Survey on Heterogeneous Graph Embedding: Methods, Techniques, Applications and Sources
Heterogeneous graphs (HGs) also known as heterogeneous information networks have become ubiquitous in real-world scenarios; therefore, HG embedding, which aims to learn representations in a lower-dimension space while preserving the heterogeneous structures and semantics for downstream tasks (e. g., node/graph classification, node clustering, link prediction), has drawn considerable attentions in recent years.
Joint Semantics and Data-Driven Path Representation for Knowledge Graph Inference
Inference on a large-scale knowledge graph (KG) is of great importance for KG applications like question answering.
Heterogeneous Graph Neural Network for Recommendation
The prosperous development of e-commerce has spawned diverse recommendation systems.
AM-GCN: Adaptive Multi-channel Graph Convolutional Networks
We tackle the challenge and propose an adaptive multi-channel graph convolutional networks for semi-supervised classification (AM-GCN).
Graph Neural News Recommendation with Unsupervised Preference Disentanglement
Furthermore, the learned representations are disentangled with latent preference factors by a neighborhood routing algorithm, which can enhance expressiveness and interpretability.
Decorrelated Clustering with Data Selection Bias
Most of existing clustering algorithms are proposed without considering the selection bias in data.
Structural Deep Clustering Network
The strength of deep clustering methods is to extract the useful representations from the data itself, rather than the structure of data, which receives scarce attention in representation learning.
Hyperbolic Graph Attention Network
Graph neural network (GNN) has shown superior performance in dealing with graphs, which has attracted considerable research attention recently.
Multi-Component Graph Convolutional Collaborative Filtering
The interactions of users and items in recommender system could be naturally modeled as a user-item bipartite graph.
GraLSP: Graph Neural Networks with Local Structural Patterns
Specifically, we capture local graph structures via random anonymous walks, powerful and flexible tools that represent structural patterns.
Improving Distantly-Supervised Relation Extraction with Joint Label Embedding
Distantly-supervised relation extraction has proven to be effective to find relational facts from texts.
Heterogeneous Graph Attention Networks for Semi-supervised Short Text Classification
Then, we propose Heterogeneous Graph ATtention networks (HGAT) to embed the HIN for short text classification based on a dual-level attention mechanism, including node-level and type-level attentions.
Graph Neural News Recommendation with Long-term and Short-term Interest Modeling
Existing methods on news recommendation mainly include collaborative filtering methods which rely on direct user-item interactions and content based methods which characterize the content of user reading history.
Deep Collaborative Filtering with Multi-Aspect Information in Heterogeneous Networks
However, the characteristics of users and the properties of items may stem from different aspects, e. g., the brand-aspect and category-aspect of items.
Temporal Network Embedding with Micro- and Macro-dynamics
The micro-dynamics describe the formation process of network structures in a detailed manner, while the macro-dynamics refer to the evolution pattern of the network scale.
Relation Structure-Aware Heterogeneous Information Network Embedding
In this paper, we take the structural characteristics of heterogeneous relations into consideration and propose a novel Relation structure-aware Heterogeneous Information Network Embedding model (RHINE).
Heterogeneous Graph Attention Network
With the learned importance from both node-level and semantic-level attention, the importance of node and meta-path can be fully considered.
Ranked #1 on
Heterogeneous Node Classification
on DBLP (PACT) 14k
Social and Information Networks
Heterogeneous Information Network Embedding for Recommendation
In this paper, we propose a novel heterogeneous network embedding based approach for HIN based recommendation, called HERec.
Social and Information Networks
HeteSim: A General Framework for Relevance Measure in Heterogeneous Networks
Similarity search is an important function in many applications, which usually focuses on measuring the similarity between objects with the same type.
