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View article: Cluster-guided Contrastive Class-imbalanced Graph Classification
Cluster-guided Contrastive Class-imbalanced Graph Classification Open
This paper studies the problem of class-imbalanced graph classification, which aims at effectively classifying the graph categories in scenarios with imbalanced class distributions. While graph neural networks (GNNs) have achieved remarkab…
View article: GeoMamba: Towards Multi-granular POI Recommendation with Geographical State Space Model
GeoMamba: Towards Multi-granular POI Recommendation with Geographical State Space Model Open
Point-of-Interest (POI) recommendation plays an important role in a wide range of location-based social network ap- plications, aiming to accurately predicting users’ next visits based on their historical check-in records. Previous efforts…
View article: A Social Dynamical System for Twitter Analysis
A Social Dynamical System for Twitter Analysis Open
Understanding the evolution of public opinion is crucial for informed decision-making in various domains, particularly public affairs. The rapid growth of social networks, such as Twitter (now rebranded as X), provides an unprecedented opp…
View article: Cluster-guided Contrastive Class-imbalanced Graph Classification
Cluster-guided Contrastive Class-imbalanced Graph Classification Open
This paper studies the problem of class-imbalanced graph classification, which aims at effectively classifying the graph categories in scenarios with imbalanced class distributions. While graph neural networks (GNNs) have achieved remarkab…
View article: Towards Graph Contrastive Learning: A Survey and Beyond
Towards Graph Contrastive Learning: A Survey and Beyond Open
In recent years, deep learning on graphs has achieved remarkable success in various domains. However, the reliance on annotated graph data remains a significant bottleneck due to its prohibitive cost and time-intensive nature. To address t…
View article: Hypergraph-enhanced Dual Semi-supervised Graph Classification
Hypergraph-enhanced Dual Semi-supervised Graph Classification Open
In this paper, we study semi-supervised graph classification, which aims at accurately predicting the categories of graphs in scenarios with limited labeled graphs and abundant unlabeled graphs. Despite the promising capability of graph ne…
View article: A Survey of Graph Neural Networks in Real world: Imbalance, Noise, Privacy and OOD Challenges
A Survey of Graph Neural Networks in Real world: Imbalance, Noise, Privacy and OOD Challenges Open
Graph-structured data exhibits universality and widespread applicability across diverse domains, such as social network analysis, biochemistry, financial fraud detection, and network security. Significant strides have been made in leveragi…
View article: COOL: A Conjoint Perspective on Spatio-Temporal Graph Neural Network for Traffic Forecasting
COOL: A Conjoint Perspective on Spatio-Temporal Graph Neural Network for Traffic Forecasting Open
This paper investigates traffic forecasting, which attempts to forecast the future state of traffic based on historical situations. This problem has received ever-increasing attention in various scenarios and facilitated the development of…
View article: GPS: Graph Contrastive Learning via Multi-scale Augmented Views from Adversarial Pooling
GPS: Graph Contrastive Learning via Multi-scale Augmented Views from Adversarial Pooling Open
Self-supervised graph representation learning has recently shown considerable promise in a range of fields, including bioinformatics and social networks. A large number of graph contrastive learning approaches have shown promising performa…
View article: PolyCF: Towards the Optimal Spectral Graph Filters for Collaborative Filtering
PolyCF: Towards the Optimal Spectral Graph Filters for Collaborative Filtering Open
Collaborative Filtering (CF) is a pivotal research area in recommender systems that capitalizes on collaborative similarities between users and items to provide personalized recommendations. With the remarkable achievements of node embeddi…
View article: Graph Neural Networks in Intelligent Transportation Systems: Advances, Applications and Trends
Graph Neural Networks in Intelligent Transportation Systems: Advances, Applications and Trends Open
Intelligent Transportation System (ITS) is crucial for improving traffic congestion, reducing accidents, optimizing urban planning, and more. However, the complexity of traffic networks has rendered traditional machine learning and statist…
View article: ALEX: Towards Effective Graph Transfer Learning with Noisy Labels
ALEX: Towards Effective Graph Transfer Learning with Noisy Labels Open
Graph Neural Networks (GNNs) have garnered considerable interest due to their exceptional performance in a wide range of graph machine learning tasks. Nevertheless, the majority of GNN-based approaches have been examined using well-annotat…
View article: Toward Effective Semi-supervised Node Classification with Hybrid Curriculum Pseudo-labeling
Toward Effective Semi-supervised Node Classification with Hybrid Curriculum Pseudo-labeling Open
Semi-supervised node classification is a crucial challenge in relational data mining and has attracted increasing interest in research on graph neural networks (GNNs). However, previous approaches merely utilize labeled nodes to supervise …
View article: ALEX: Towards Effective Graph Transfer Learning with Noisy Labels
ALEX: Towards Effective Graph Transfer Learning with Noisy Labels Open
Graph Neural Networks (GNNs) have garnered considerable interest due to their exceptional performance in a wide range of graph machine learning tasks. Nevertheless, the majority of GNN-based approaches have been examined using well-annotat…
View article: Redundancy-Free Self-Supervised Relational Learning for Graph Clustering
Redundancy-Free Self-Supervised Relational Learning for Graph Clustering Open
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.…
View article: RAHNet: Retrieval Augmented Hybrid Network for Long-tailed Graph Classification
RAHNet: Retrieval Augmented Hybrid Network for Long-tailed Graph Classification Open
Graph classification is a crucial task in many real-world multimedia applications, where graphs can represent various multimedia data types such as images, videos, and social networks. Previous efforts have applied graph neural networks (G…
View article: GLCC: A General Framework for Graph-Level Clustering
GLCC: A General Framework for Graph-Level Clustering Open
This paper studies the problem of graph-level clustering, which is a novel yet challenging task. This problem is critical in a variety of real-world applications such as protein clustering and genome analysis in bioinformatics. Recent year…
View article: Learning on Graphs under Label Noise
Learning on Graphs under Label Noise Open
Node classification on graphs is a significant task with a wide range of applications, including social analysis and anomaly detection. Even though graph neural networks (GNNs) have produced promising results on this task, current techniqu…
View article: Towards Semi-Supervised Universal Graph Classification
Towards Semi-Supervised Universal Graph Classification Open
Graph neural networks have pushed state-of-the-arts in graph classifications\nrecently. Typically, these methods are studied within the context of supervised\nend-to-end training, which necessities copious task-specific labels. However,\ni…
View article: A Diffusion model for POI recommendation
A Diffusion model for POI recommendation Open
Next Point-of-Interest (POI) recommendation is a critical task in location-based services that aim to provide personalized suggestions for the user's next destination. Previous works on POI recommendation have laid focused on modeling the …
View article: Learning Graph ODE for Continuous-Time Sequential Recommendation
Learning Graph ODE for Continuous-Time Sequential Recommendation Open
Sequential recommendation aims at understanding user preference by capturing successive behavior correlations, which are usually represented as the item purchasing sequences based on their past interactions. Existing efforts generally pred…
View article: A Comprehensive Survey on Deep Graph Representation Learning
A Comprehensive Survey on Deep Graph Representation Learning Open
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 lea…
View article: Focus on Informative Graphs! Semi-Supervised Active Learning for Graph-Level Classification
Focus on Informative Graphs! Semi-Supervised Active Learning for Graph-Level Classification Open
View article: Focus on Informative Graphs! Semi-Supervised Active Learning for Graph-Level Classification
Focus on Informative Graphs! Semi-Supervised Active Learning for Graph-Level Classification Open
View article: Drtp: A Generic Differentiated Reliable Transport Protocol
Drtp: A Generic Differentiated Reliable Transport Protocol Open
View article: DisenPOI: Disentangling Sequential and Geographical Influence for Point-of-Interest Recommendation
DisenPOI: Disentangling Sequential and Geographical Influence for Point-of-Interest Recommendation Open
Point-of-Interest (POI) recommendation plays a vital role in various location-aware services. It has been observed that POI recommendation is driven by both sequential and geographical influences. However, since there is no annotated label…
View article: GLCC: A General Framework for Graph-Level Clustering
GLCC: A General Framework for Graph-Level Clustering Open
This paper studies the problem of graph-level clustering, which is a novel yet challenging task. This problem is critical in a variety of real-world applications such as protein clustering and genome analysis in bioinformatics. Recent year…
View article: Kernel-based Substructure Exploration for Next POI Recommendation
Kernel-based Substructure Exploration for Next POI Recommendation Open
Point-of-Interest (POI) recommendation, which benefits from the proliferation of GPS-enabled devices and location-based social networks (LBSNs), plays an increasingly important role in recommender systems. It aims to provide users with the…
View article: Arbutin-modified microspheres prevent osteoarthritis progression by mobilizing local anti-inflammatory and antioxidant responses
Arbutin-modified microspheres prevent osteoarthritis progression by mobilizing local anti-inflammatory and antioxidant responses Open
Osteoarthritis (OA) is a common degenerative joint disease worldwide and currently there is no effective strategy to stop its progression. It is known that oxidative stress and inflammation can promote the development of OA, and therapeuti…
View article: Arbutin-Modified Microspheres Prevent Osteoarthritis Progression by Mobilizing Local Anti-Inflammatory and Antioxidant Responses
Arbutin-Modified Microspheres Prevent Osteoarthritis Progression by Mobilizing Local Anti-Inflammatory and Antioxidant Responses Open
Osteoarthritis (OA), a common chronic degenerative disease, is considered the leading cause of disability worldwide. However, the current clinical treatments for OA are still unsatisfactory. Arbutin (ARB) is a major active ingredient of th…