Q
Quan Fang
Researcher at Chinese Academy of Sciences
Publications - 55
Citations - 1045
Quan Fang is an academic researcher from Chinese Academy of Sciences. The author has contributed to research in topics: Computer science & Recommender system. The author has an hindex of 14, co-authored 42 publications receiving 511 citations.
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Journal ArticleDOI
Topic-Sensitive Influencer Mining in Interest-Based Social Media Networks via Hypergraph Learning
TL;DR: A novel Topic-Sensitive Influencer Mining (TSIM) framework in interest-based social media networks that aims to find topical influential users and images and demonstrates the effectiveness of the proposed framework on a real-world dataset.
Proceedings ArticleDOI
Fake News Detection via Knowledge-driven Multimodal Graph Convolutional Networks
TL;DR: A novel Knowledge-driven Multimodal Graph Convolutional Network (KMGCN) is proposed to model the semantic representations by jointly modeling the textual information, knowledge concepts and visual information into a unified framework for fake news detection.
Proceedings ArticleDOI
Multi-modal Knowledge-aware Event Memory Network for Social Media Rumor Detection
TL;DR: A novel Multimodal Knowledge-aware Event Memory Network (MKEMN) which utilizes the multi-modal representation of the post on social media and retrieves external knowledge from real-world knowledge graph to complement the semantic representation of short texts of posts and takes conceptual knowledge as additional evidence to improve rumor detection.
Proceedings ArticleDOI
Explainable Interaction-driven User Modeling over Knowledge Graph for Sequential Recommendation
TL;DR: A novel Explainable Interaction-driven User Modeling (EIUM) algorithm to exploit Knowledge Graph for constructing an effective and explainable sequential recommender and captures the interaction-level user dynamic preferences by modeling the sequential interactions.
Proceedings ArticleDOI
CSAN: Contextual Self-Attention Network for User Sequential Recommendation
TL;DR: A unified Contextual Self-Attention Network (CSAN) is proposed to address the three properties of heterogeneous user behaviors, which are projected into a common latent semantic space and fed into the feature-wise self-attention network to capture the polysemy of user behaviors.