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Self-Supervised Multi-Channel Hypergraph Convolutional Network for Social Recommendation
Social relations are often used to improve recommendation quality when u...
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Line Graph Neural Networks for Link Prediction
We consider the graph link prediction task, which is a classic graph ana...
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Scalable Attack on Graph Data by Injecting Vicious Nodes
Recent studies have shown that graph convolution networks (GCNs) are vul...
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Enhance Social Recommendation with Adversarial Graph Convolutional Networks
Recent reports from industry show that social recommender systems consis...
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Generating Reliable Friends via Adversarial Training to Improve Social Recommendation
Most of the recent studies of social recommendation assume that people s...
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Graph Neural Networks with High-order Feature Interactions
Network representation learning, a fundamental research problem which ai...
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SpecAE: Spectral AutoEncoder for Anomaly Detection in Attributed Networks
Anomaly detection aims to distinguish observations that are rare and dif...
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Deep Structured Cross-Modal Anomaly Detection
Anomaly detection is a fundamental problem in data mining field with man...
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Explaining Vulnerabilities to Adversarial Machine Learning through Visual Analytics
Machine learning models are currently being deployed in a variety of rea...
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Learning Individual Treatment Effects from Networked Observational Data
With convenient access to observational data, learning individual causal...
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Online Newton Step Algorithm with Estimated Gradient
Online learning with limited information feedback (bandit) tries to solv...
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A Survey of Learning Causality with Data: Problems and Methods
The era of big data provides researchers with convenient access to copio...
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Multi-Level Network Embedding with Boosted Low-Rank Matrix Approximation
As opposed to manual feature engineering which is tedious and difficult ...
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Attributed Network Embedding for Learning in a Dynamic Environment
Network embedding leverages the node proximity manifested to learn a low...
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