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20202024
most citedAttention Is Not the Only Choice: Counterfactual Reasoning for Path-Based Explainable Recommendation

45 citations · 142 across the 10 of their papers we have counts for

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Showing cs.SIShow all

6 papers · 1 filter

cs.SI2023

All in One: Multi-task Prompting for Graph Neural Networks

Xiangguo Sun, Hong Cheng, Jia Li +2

Recently, ''pre-training and fine-tuning'' has been adopted as a standard workflow for many graph tasks since it can take general graph knowledge to relieve the lack of graph annot…

cs.SI2023★ 2 cited

Self-supervised Hypergraph Representation Learning for Sociological Analysis

Xiangguo Sun, Hong Cheng, Bo Liu +4

Modern sociology has profoundly uncovered many convincing social criteria for behavioural analysis. Unfortunately, many of them are too subjective to be measured and presented in o…

cs.SI2021★ 1 cited

Hyperbolic Hypergraphs for Sequential Recommendation

Yicong Li, Hongxu Chen, Xiangguo Sun +5

Hypergraphs have been becoming a popular choice to model complex, non-pairwise, and higher-order interactions for recommender system. However, compared with traditional graph-based…

cs.SI2021★ 1 cited

Temporal Meta-path Guided Explainable Recommendation

Hongxu Chen, Yicong Li, Xiangguo Sun +2

This paper utilizes well-designed item-item path modelling between consecutive items with attention mechanisms to sequentially model dynamic user-item evolutions on dynamic knowled…

cs.SI2020

Heterogeneous Hypergraph Embedding for Graph Classification

Xiangguo Sun, Hongzhi Yin, Bo Liu +4

Recently, graph neural networks have been widely used for network embedding because of their prominent performance in pairwise relationship learning. In the real world, a more natu…

cs.SI2020★ 7 cited

Multi-level Graph Convolutional Networks for Cross-platform Anchor Link Prediction

Hongxu Chen, Hongzhi Yin, Xiangguo Sun +3

Cross-platform account matching plays a significant role in social network analytics, and is beneficial for a wide range of applications. However, existing methods either heavily r…