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Found 26 papers, 6 papers with code
A Survey of Graph Neural Networks in Real world: Imbalance, Noise, Privacy and OOD Challenges
To tackle these issues, substantial efforts have been devoted to improving the performance of GNN models in practical real-world scenarios, as well as enhancing their reliability and robustness.
COOL: A Conjoint Perspective on Spatio-Temporal Graph Neural Network for Traffic Forecasting
Toward this end, this paper proposes Conjoint Spatio-Temporal graph neural network (abbreviated as COOL), which models heterogeneous graphs from prior and posterior information to conjointly capture high-order spatio-temporal relationships.
A Survey of Data-Efficient Graph Learning
Graph-structured data, prevalent in domains ranging from social networks to biochemical analysis, serve as the foundation for diverse real-world systems.
GPS: Graph Contrastive Learning via Multi-scale Augmented Views from Adversarial Pooling
Self-supervised graph representation learning has recently shown considerable promise in a range of fields, including bioinformatics and social networks.
PolyCF: Towards the Optimal Spectral Graph Filters for Collaborative Filtering
Collaborative Filtering (CF) is a pivotal research area in recommender systems that capitalizes on collaborative similarities between users and items to provide personalized recommendations.
A Survey on Graph Neural Networks in Intelligent Transportation Systems
However, most of the research in this area is still concentrated on traffic forecasting, while other ITS domains, such as autonomous vehicles and urban planning, still require more attention.
Graph ODE with Factorized Prototypes for Modeling Complicated Interacting Dynamics
In this paper, we propose a new approach named Graph ODE with factorized prototypes (GOAT) to address the problem.
ALEX: Towards Effective Graph Transfer Learning with Noisy Labels
Nevertheless, the majority of GNN-based approaches have been examined using well-annotated benchmark datasets, leading to suboptimal performance in real-world graph learning scenarios.
Dynamic Hypergraph Structure Learning for Traffic Flow Forecasting
This paper studies the problem of traffic flow forecasting, which aims to predict future traffic conditions on the basis of road networks and traffic conditions in the past.
Redundancy-Free Self-Supervised Relational Learning for Graph Clustering
Graph clustering, which learns the node representations for effective cluster assignments, is a fundamental yet challenging task in data analysis and has received considerable attention accompanied by graph neural networks in recent years.
FIMO: A Challenge Formal Dataset for Automated Theorem Proving
We present FIMO, an innovative dataset comprising formal mathematical problem statements sourced from the International Mathematical Olympiad (IMO) Shortlisted Problems.
Towards Long-Tailed Recognition for Graph Classification via Collaborative Experts
Graph classification, aiming at learning the graph-level representations for effective class assignments, has received outstanding achievements, which heavily relies on high-quality datasets that have balanced class distribution.
RAHNet: Retrieval Augmented Hybrid Network for Long-tailed Graph Classification
Recent approaches mainly focus on re-balancing different classes during model training, which fails to explicitly introduce new knowledge and sacrifices the performance of the head classes.
Learning on Graphs under Label Noise
Since this regularization term cannot utilize label information, it can enhance the robustness of node representations to label noise.
Towards Semi-supervised Universal Graph Classification
To tackle class shifts, we estimate the certainty of unlabeled graphs using multiple subgraphs, which facilities the discovery of unlabeled data from unknown categories.
TGNN: A Joint Semi-supervised Framework for Graph-level Classification
The two twin modules collaborate with each other by exchanging instance similarity knowledge to fully explore the structure information of both labeled and unlabeled data.
A Diffusion model for POI recommendation
In this paper, we propose Diff-POI: a Diffusion-based model that samples the user's spatial preference for the next POI recommendation.
Learning Graph ODE for Continuous-Time Sequential Recommendation
Technically, GDERec is characterized by an autoregressive graph ordinary differential equation consisting of two components, which are parameterized by two tailored graph neural networks (GNNs) respectively to capture user preference from the perspective of hybrid dynamical systems.
A Comprehensive Survey on Deep Graph Representation Learning
Graph representation learning aims to effectively encode high-dimensional sparse graph-structured data into low-dimensional dense vectors, which is a fundamental task that has been widely studied in a range of fields, including machine learning and data mining.
Robust Dancer: Long-term 3D Dance Synthesis Using Unpaired Data
How to automatically synthesize natural-looking dance movements based on a piece of music is an incrementally popular yet challenging task.
DisenPOI: Disentangling Sequential and Geographical Influence for Point-of-Interest Recommendation
Point-of-Interest (POI) recommendation plays a vital role in various location-aware services.
GLCC: A General Framework for Graph-Level Clustering
In this paper, we propose a general graph-level clustering framework named Graph-Level Contrastive Clustering (GLCC) given multiple graphs.
Kernel-based Substructure Exploration for Next POI Recommendation
To tackle the above issues, we propose a Kernel-Based Graph Neural Network (KBGNN) for next POI recommendation, which combines the characteristics of both geographical and sequential influences in a collaborative way.
KGNN: Harnessing Kernel-based Networks for Semi-supervised Graph Classification
This problem is typically solved by using graph neural networks (GNNs), which yet rely on a large number of labeled graphs for training and are unable to leverage unlabeled graphs.
A Note on Comparison of F-measures
We comment on a recent TKDE paper "Linear Approximation of F-measure for the Performance Evaluation of Classification Algorithms on Imbalanced Data Sets", and make two improvements related to comparison of F-measures for two prediction rules.
When does MAML Work the Best? An Empirical Study on Model-Agnostic Meta-Learning in NLP Applications
Model-Agnostic Meta-Learning (MAML), a model-agnostic meta-learning method, is successfully employed in NLP applications including few-shot text classification and multi-domain low-resource language generation.
