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Found 16 papers, 5 papers with code
Do We Trust What They Say or What They Do? A Multimodal User Embedding Provides Personalized Explanations
Our work paves the way for more explainable, reliable, and effective social media user embedding which allows for better personalized content delivery.
Rank and Align: Towards Effective Source-free Graph Domain Adaptation
To this end, we investigate an underexplored yet practical problem of source-free graph domain adaptation, which transfers knowledge from source models instead of source graphs to a target domain.
Towards Graph Contrastive Learning: A Survey and Beyond
In recent years, deep learning on graphs has achieved remarkable success in various domains.
Hypergraph-enhanced Dual Semi-supervised Graph Classification
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.
Fast Inference of Removal-Based Node Influence
We propose a new method of evaluating node influence, which measures the prediction change of a trained GNN model caused by removing a node.
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.
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.
PGODE: Towards High-quality System Dynamics Modeling
In this paper, we propose a new approach named Prototypical Graph ODE (PGODE) to address the problem.
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.
Detecting Political Biases of Named Entities and Hashtags on Twitter
We examine a variety of applications and we thereby demonstrate the effectiveness of our PEM model.
TIMME: Twitter Ideology-detection via Multi-task Multi-relational Embedding
However, the incompleteness of the labels and the features in social network datasets is tricky, not to mention the enormous data size and the heterogeneousity.
Session-based Social Recommendation via Dynamic Graph Attention Networks
However, recommendation in online communities is a challenging problem: 1) users' interests are dynamic, and 2) users are influenced by their friends.
Ranked #1 on
Recommendation Systems
on Douban
(NDCG metric)
AutoInt: Automatic Feature Interaction Learning via Self-Attentive Neural Networks
Afterwards, a multi-head self-attentive neural network with residual connections is proposed to explicitly model the feature interactions in the low-dimensional space.
Ranked #4 on
Click-Through Rate Prediction
on KKBox
