Philip S Yu
Biographic Data
| ID | 3925732 |
|---|---|
| NAME | Philip S Yu |
| GIVEN NAMES | Philip S |
| FAMILY NAME | Yu |
| SIGNATURE | YU P S |
| AFFILIATIONS | University of Illinois Chicago |
| ORCID | 0000-0002-3491-5968 |
| VERIFIED | Yes |
| TOTAL WORKS | 12 |
| TOTAL CITATIONS | 6 |
| AUTHOR COUNT | 11 |
| EDITOR COUNT | 1 |
| FIRST PUBLICATION YEAR | 2008 |
| LATEST PUBLICATION YEAR | 2026 |
| H-INDEX | 1 |
Loki’s Dance of Illusions: A Comprehensive Survey of Hallucination in Large Language Models
Edgar Allan Poe noted, “Truth often lurks in the shadow of error,” highlighting the deep complexity intrinsic to the interplay between truth and falsehood, notably under conditions of cognitive and informational asymmetry. This dynamic is strikingly evident in large language models (LLMs). Despite their impressive linguistic generation capabilities, LLMs sometimes produce information that appears factually accurate but is, in reality, fabricated,…
Knowledge-Aware Graph Prompt Tuning for Cross-Domain Recommendation
Cross-domain recommendation (CDR) has received attention to solve the cold-start and data sparsity problems. Existing methods mainly focus on the information about overlapping users or items, neglecting to effectively and efficiently utilize the information about nonoverlapping users or items in the source domain. Currently, graph prompt learning is proposed to bridge the gap between the pretrained tasks and downstream tasks, which can fully use …
A Survey on Evaluation of Large Language Models
Large language models (LLMs) are gaining increasing popularity in both academia and industry, owing to their unprecedented performance in various applications. As LLMs continue to play a vital role in both research and daily use, their evaluation becomes increasingly critical, not only at the task level, but also at the society level for better understanding of their potential risks. Over the past years, significant efforts have been made to exam…
A Comprehensive Survey on Graph Neural Networks
Deep learning has revolutionized many machine learning tasks in recent years, ranging from image classification and video processing to speech recognition and natural language understanding. The data in these tasks are typically represented in the Euclidean space. However, there is an increasing number of applications, where data are generated from non-Euclidean domains and are represented as graphs with complex relationships and interdependency …
Community Hiding by Link Perturbation in Social Networks
Complex social network is a kind of relationship system composed of many nodes according to social relations. Community detection helps scholars to understand this network topology and find out meaningful communities. Many scholars are therefore actively exploring new community detection algorithms. However, it brings privacy issues such as the disclosure of personal or group information of community members and goes against an individual or grou…
Broad Learning Through Fusions: An Application on Social Networks
Information Diffusion
Deception detection in Twitter
Graph Classification in Heterogeneous Networks
Modeling blogger influence in a community
Next Generation of Data Mining
Drawn from the US National Science Foundations Symposium on Next Generation of Data Mining and Cyber-Enabled Discovery for Innovation (NGDM 07), Next Generation of Data Mining explores emerging technologies and applications in data mining as well as potential challenges faced by the field. Gathering perspectives from top experts across different disciplines, the book debates upcoming challenges and outlines computational methods. The contributors…
Top 10 algorithms in data mining
Next Generation of Data Mining
Drawn from the US National Science Foundations Symposium on Next Generation of Data Mining and Cyber-Enabled Discovery for Innovation (NGDM 07), Next Generation of Data Mining explores emerging technologies and applications in data mining as well as potential challenges faced by the field. Gathering perspectives from top experts across different disciplines, the book debates upcoming challenges and outlines computational methods. The contributors…
Top 10 algorithms in data mining
Modeling blogger influence in a community
Graph Classification in Heterogeneous Networks
Deception detection in Twitter
Broad Learning Through Fusions: An Application on Social Networks
Information Diffusion
A Comprehensive Survey on Graph Neural Networks
Deep learning has revolutionized many machine learning tasks in recent years, ranging from image classification and video processing to speech recognition and natural language understanding. The data in these tasks are typically represented in the Euclidean space. However, there is an increasing number of applications, where data are generated from non-Euclidean domains and are represented as graphs with complex relationships and interdependency …
Community Hiding by Link Perturbation in Social Networks
Complex social network is a kind of relationship system composed of many nodes according to social relations. Community detection helps scholars to understand this network topology and find out meaningful communities. Many scholars are therefore actively exploring new community detection algorithms. However, it brings privacy issues such as the disclosure of personal or group information of community members and goes against an individual or grou…
A Survey on Evaluation of Large Language Models
Large language models (LLMs) are gaining increasing popularity in both academia and industry, owing to their unprecedented performance in various applications. As LLMs continue to play a vital role in both research and daily use, their evaluation becomes increasingly critical, not only at the task level, but also at the society level for better understanding of their potential risks. Over the past years, significant efforts have been made to exam…
Knowledge-Aware Graph Prompt Tuning for Cross-Domain Recommendation
Cross-domain recommendation (CDR) has received attention to solve the cold-start and data sparsity problems. Existing methods mainly focus on the information about overlapping users or items, neglecting to effectively and efficiently utilize the information about nonoverlapping users or items in the source domain. Currently, graph prompt learning is proposed to bridge the gap between the pretrained tasks and downstream tasks, which can fully use …
Loki’s Dance of Illusions: A Comprehensive Survey of Hallucination in Large Language Models
Edgar Allan Poe noted, “Truth often lurks in the shadow of error,” highlighting the deep complexity intrinsic to the interplay between truth and falsehood, notably under conditions of cognitive and informational asymmetry. This dynamic is strikingly evident in large language models (LLMs). Despite their impressive linguistic generation capabilities, LLMs sometimes produce information that appears factually accurate but is, in reality, fabricated,…
Computer Science (9 works) · Artificial Intelligence (4 works) · Complex Network Analysis Techniques (4 works) · Data mining (4 works) · Data science (4 works) · World Wide Web (4 works) · Advanced Graph Neural Networks (3 works) · Graph (3 works) · Opinion Dynamics and Social Influence (3 works) · Social media (3 works)