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07/10/2023
Fairness and Diversity in Recommender Systems: A Survey
Recommender systems are effective tools for mitigating information overl...
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06/02/2023
A Survey on Explainability of Graph Neural Networks
Graph neural networks (GNNs) are powerful graph-based deep-learning mode...
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04/03/2023
Counterfactual Learning on Graphs: A Survey
Graph-structured data are pervasive in the real-world such as social net...
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01/14/2023
A Comprehensive Survey of Graph-level Learning
Graphs have a superior ability to represent relational data, like chemic...
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08/03/2022
Link Prediction on Heterophilic Graphs via Disentangled Representation Learning
Link prediction is an important task that has wide applications in vario...
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06/15/2022
Feature Overcorrelation in Deep Graph Neural Networks: A New Perspective
Recent years have witnessed remarkable success achieved by graph neural ...
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05/31/2022
Graph-level Neural Networks: Current Progress and Future Directions
Graph-structured data consisting of objects (i.e., nodes) and relationsh...
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05/22/2022
Sequential/Session-based Recommendations: Challenges, Approaches, Applications and Opportunities
In recent years, sequential recommender systems (SRSs) and session-based...
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02/22/2022
Graph Lifelong Learning: A Survey
Graph learning substantially contributes to solving artificial intellige...
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10/22/2021
Distance-wise Prototypical Graph Neural Network in Node Imbalance Classification
Recent years have witnessed the significant success of applying graph ne...
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08/07/2021
Jointly Attacking Graph Neural Network and its Explanations
Graph Neural Networks (GNNs) have boosted the performance for many graph...
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07/21/2021
Bridging the Gap between Spatial and Spectral Domains: A Theoretical Framework for Graph Neural Networks
During the past decade, deep learning's performance has been widely reco...
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06/08/2021
NRGNN: Learning a Label Noise-Resistant Graph Neural Network on Sparsely and Noisily Labeled Graphs
Graph Neural Networks (GNNs) have achieved promising results for semi-su...
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05/10/2021
Graph Feature Gating Networks
Graph neural networks (GNNs) have received tremendous attention due to t...
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02/10/2021
Information Prediction using Knowledge Graphs for Contextual Malware Threat Intelligence
Large amounts of threat intelligence information about mal-ware attacks ...
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06/28/2020
Graph Convolutional Networks against Degree-Related Biases
In recent years, Graph Convolutional Networks (GCNs) show competitive pe...
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06/20/2020
MALOnt: An Ontology for Malware Threat Intelligence
Malware threat intelligence uncovers deep information about malware, thr...
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05/22/2020
Non-IID Graph Neural Networks
Graph classification is an important task on graph-structured data with ...
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11/25/2019
Efficient Global String Kernel with Random Features: Beyond Counting Substructures
Analysis of large-scale sequential data has been one of the most crucial...
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11/25/2019
Scalable Global Alignment Graph Kernel Using Random Features: From Node Embedding to Graph Embedding
Graph kernels are widely used for measuring the similarity between graph...
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11/22/2019
Joint Modeling of Local and Global Temporal Dynamics for Multivariate Time Series Forecasting with Missing Values
Multivariate time series (MTS) forecasting is widely used in various dom...
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10/22/2019
How can AI Automate End-to-End Data Science?
Data science is labor-intensive and human experts are scarce but heavily...
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05/25/2018
Scalable Spectral Clustering Using Random Binning Features
Spectral clustering is one of the most effective clustering approaches t...
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05/17/2017
REMIX: Automated Exploration for Interactive Outlier Detection
Outlier detection is the identification of points in a dataset that do n...
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03/22/2017
Joint Intermodal and Intramodal Label Transfers for Extremely Rare or Unseen Classes
In this paper, we present a label transfer model from texts to images fo...
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11/24/2015