Publications (24)
Tagvisor: A Privacy Advisor for Sharing Hashtags
Yang Zhang, Mathias Humbert, Tahleen Rahman +3
Hashtag has emerged as a widely used concept of popular culture and campaigns, but its implications on people's privacy have not been investigated so far. In this paper, we present…
FinGAT: Financial Graph Attention Networks for Recommending Top-K Profitable Stocks
Yi-Ling Hsu, Yu-Che Tsai, Cheng-Te Li
Financial technology (FinTech) has drawn much attention among investors and companies. While conventional stock analysis in FinTech targets at predicting stock prices, less effort…
RetaGNN: Relational Temporal Attentive Graph Neural Networks for Holistic Sequential Recommendation
Cheng Hsu, Cheng-Te Li
Sequential recommendation (SR) is to accurately recommend a list of items for a user based on her current accessed ones. While new-coming users continuously arrive in the real worl…
TabGSL: Graph Structure Learning for Tabular Data Prediction
Jay Chiehen Liao, Cheng-Te Li
This work presents a novel approach to tabular data prediction leveraging graph structure learning and graph neural networks. Despite the prevalence of tabular data in real-world a…
DDNAS: Discretized Differentiable Neural Architecture Search for Text Classification
Kuan-Chun Chen, Cheng-Te Li, Kuo-Jung Lee
Neural Architecture Search (NAS) has shown promising capability in learning text representation. However, existing text-based NAS neither performs a learnable fusion of neural oper…
AGSTN: Learning Attention-adjusted Graph Spatio-Temporal Networks for Short-term Urban Sensor Value Forecasting
Yi-Ju Lu, Cheng-Te Li
Forecasting spatio-temporal correlated time series of sensor values is crucial in urban applications, such as air pollution alert, biking resource management, and intelligent trans…
SUVR: A Search-based Approach to Unsupervised Visual Representation Learning
Yi-Zhan Xu, Chih-Yao Chen, Cheng-Te Li
Unsupervised learning has grown in popularity because of the difficulty of collecting annotated data and the development of modern frameworks that allow us to learn from unlabeled…
WikiContradiction: Detecting Self-Contradiction Articles on Wikipedia
Cheng Hsu, Cheng-Te Li, Diego Saez-Trumper +1
While Wikipedia has been utilized for fact-checking and claim verification to debunk misinformation and disinformation, it is essential to either improve article quality and rule o…
FairSR: Fairness-aware Sequential Recommendation through Multi-Task Learning with Preference Graph Embeddings
Cheng-Te Li, Cheng Hsu, Yang Zhang
Sequential recommendation (SR) learns from the temporal dynamics of user-item interactions to predict the next ones. Fairness-aware recommendation mitigates a variety of algorithmi…
A Comprehensive Approach to Unsupervised Embedding Learning based on AND Algorithm
Sungwon Han, Yizhan Xu, Sungwon Park +2
Unsupervised embedding learning aims to extract good representation from data without the need for any manual labels, which has been a critical challenge in many supervised learnin…
Hierarchical Message-Passing Graph Neural Networks
Zhiqiang Zhong, Cheng-Te Li, Jun Pang
Graph Neural Networks (GNNs) have become a prominent approach to machine learning with graphs and have been increasingly applied in a multitude of domains. Nevertheless, since most…
ZS-BERT: Towards Zero-Shot Relation Extraction with Attribute Representation Learning
Chih-Yao Chen, Cheng-Te Li
While relation extraction is an essential task in knowledge acquisition and representation, and new-generated relations are common in the real world, less effort is made to predict…
An Embarrassingly Simple Rule-based Visiting Circulation Approach to Trip Destination Prediction
Eng-Shen Tu, Yong-Han Chen, En-Chao Liu +2
In this paper, we propose the Rule-based Visiting Circulation (RVC) model in tackling the challenge in the IEEE Big Data Cup 2022: Trip Destination Prediction. Given trips containi…
GraphFC: Customs Fraud Detection with Label Scarcity
Karandeep Singh, Yu-Che Tsai, Cheng-Te Li +2
Custom officials across the world encounter huge volumes of transactions. With increased connectivity and globalization, the customs transactions continue to grow every year. Assoc…
Towards a More Reliable Privacy-preserving Recommender System
Jia-Yun Jiang, Cheng-Te Li, Shou-De Lin
This paper proposes a privacy-preserving distributed recommendation framework, Secure Distributed Collaborative Filtering (SDCF), to preserve the privacy of value, model and existe…
Personalised Meta-path Generation for Heterogeneous GNNs
Zhiqiang Zhong, Cheng-Te Li, Jun Pang
Recently, increasing attention has been paid to heterogeneous graph representation learning (HGRL), which aims to embed rich structural and semantic information in heterogeneous in…
NetFense: Adversarial Defenses against Privacy Attacks on Neural Networks for Graph Data
I-Chung Hsieh, Cheng-Te Li
Recent advances in protecting node privacy on graph data and attacking graph neural networks (GNNs) gain much attention. The eye does not bring these two essential tasks together y…
CAND: Cross-Domain Ambiguity Inference for Early Detecting Nuanced Illness Deterioration
Lo Pang-Yun Ting, Zhen Tan, Hong-Pei Chen +4
Early detection of patient deterioration is essential for timely treatment, with vital signs like heart rates being key health indicators. Existing methods tend to solely analyze v…
Graph Neural Networks for Tabular Data Learning: A Survey with Taxonomy and Directions
Cheng-Te Li, Yu-Che Tsai, Chih-Yao Chen +1
In this survey, we dive into Tabular Data Learning (TDL) using Graph Neural Networks (GNNs), a domain where deep learning-based approaches have increasingly shown superior performa…
CoANE: Modeling Context Co-occurrence for Attributed Network Embedding
I-Chung Hsieh, Cheng-Te Li
Attributed network embedding (ANE) is to learn low-dimensional vectors so that not only the network structure but also node attributes can be preserved in the embedding space. Exis…
GCAN: Graph-aware Co-Attention Networks for Explainable Fake News Detection on Social Media
Yi-Ju Lu, Cheng-Te Li
This paper solves the fake news detection problem under a more realistic scenario on social media. Given the source short-text tweet and the corresponding sequence of retweet users…
HENIN: Learning Heterogeneous Neural Interaction Networks for Explainable Cyberbullying Detection on Social Media
Hsin-Yu Chen, Cheng-Te Li
In the computational detection of cyberbullying, existing work largely focused on building generic classifiers that rely exclusively on text analysis of social media sessions. Desp…
SocialNLP Fake-EmoReact 2021 Challenge Overview: Predicting Fake Tweets from Their Replies and GIFs
Chien-Kun Huang, Yi-Ting Chang, Lun-Wei Ku +2
This paper provides an overview of the Fake-EmoReact 2021 Challenge, held at the 9th SocialNLP Workshop, in conjunction with NAACL 2021. The challenge requires predicting the authe…
Multi-grained Semantics-aware Graph Neural Networks
Zhiqiang Zhong, Cheng-Te Li, Jun Pang
Graph Neural Networks (GNNs) are powerful techniques in representation learning for graphs and have been increasingly deployed in a multitude of different applications that involve…