Search Results for author: Hao Liao

Found 7 papers, 1 papers with code

Towards Fine-Grained Reasoning for Fake News Detection

1 code implementation13 Sep 2021 Yiqiao Jin, Xiting Wang, Ruichao Yang, Yizhou Sun, Wei Wang, Hao Liao, Xing Xie

The detection of fake news often requires sophisticated reasoning skills, such as logically combining information by considering word-level subtle clues.

Fake News Detection

Predicting missing links via correlation between nodes

no code implementations30 Sep 2014 Hao Liao, An Zeng, Yi-Cheng Zhang

As a fundamental problem in many different fields, link prediction aims to estimate the likelihood of an existing link between two nodes based on the observed information.

Link Prediction

Addressing Time Bias in Bipartite Graph Ranking for Important Node Identification

no code implementations28 Nov 2019 Hao Liao, Jiao Wu, Mingyang Zhou, Alexandre Vidmer

The problem of ranking the nodes in bipartite networks is valuable for many real-world applications.

Graph Ranking

Fake News Detection through Graph Comment Advanced Learning

no code implementations3 Nov 2020 Hao Liao, Qixin Liu, Kai Shu, Xing Xie

Yet, the popularity of social media also provides opportunities to better detect fake news.

Fake News Detection Representation Learning Social and Information Networks

Resisting Graph Adversarial Attack via Cooperative Homophilous Augmentation

no code implementations15 Nov 2022 Zhihao Zhu, Chenwang Wu, Min Zhou, Hao Liao, Defu Lian, Enhong Chen

Recent studies show that Graph Neural Networks(GNNs) are vulnerable and easily fooled by small perturbations, which has raised considerable concerns for adapting GNNs in various safety-critical applications.

Adversarial Attack

Aligning Large Language Models for Controllable Recommendations

no code implementations8 Mar 2024 Wensheng Lu, Jianxun Lian, Wei zhang, Guanghua Li, Mingyang Zhou, Hao Liao, Xing Xie

Inspired by the exceptional general intelligence of Large Language Models (LLMs), researchers have begun to explore their application in pioneering the next generation of recommender systems - systems that are conversational, explainable, and controllable.

Recommendation Systems

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