Xiao Huang

Unseen Anomaly Detection on Networks via Multi-Hypersphere Learning

2022 · Society for Industrial and Applied Mathematics eBooks

Network anomaly detection is a crucial task since a few anomalies can cause huge losses. Semi-supervised anomaly detection methods can effectively leverage a small number of labels as prior knowledge to enhance detection accuracy. But …

GPatch: Patching Graph Neural Networks for Cold-Start Recommendations

2022 · arXiv (Cornell University)

Cold start is an essential and persistent problem in recommender systems. State-of-the-art solutions rely on training hybrid models for both cold-start and existing users/items, based on the auxiliary information. Such a hybrid model would compromise …

FAITH: Few-Shot Graph Classification with Hierarchical Task Graphs

2022 · arXiv (Cornell University)

Few-shot graph classification aims at predicting classes for graphs, given limited labeled graphs for each class. To tackle the bottleneck of label scarcity, recent works propose to incorporate few-shot learning frameworks for fast adaptations to …

Enhancing Explainable Rating Prediction through Annotated Macro Concepts

2024

Generating recommendation reasons for recommendation results is a long-standing problem because it is challenging to explain the underlying reasons for recommending an item based on user and item IDs.Existing models usually learn semantic embeddings for …

GraphRAG-Bench: Challenging Domain-Specific Reasoning for Evaluating Graph Retrieval-Augmented Generation

2025 · arXiv (Cornell University)

Graph Retrieval Augmented Generation (GraphRAG) has garnered increasing recognition for its potential to enhance large language models (LLMs) by structurally organizing domain-specific corpora and facilitating complex reasoning. However, current evaluations of GraphRAG models predominantly rely …

Label Informed Attributed Network Embedding

2017

Attributed network embedding aims to seek low-dimensional vector representations for nodes in a network, such that original network topological structure and node attribute proximity can be preserved in the vectors. These learned representations have been …

Knowledge Graph Embedding Based Question Answering

2019

Question answering over knowledge graph (QA-KG) aims to use facts in the knowledge graph (KG) to answer natural language questions. It helps end users more efficiently and more easily access the substantial and valuable knowledge …