Meng Qu

Attending Over Triads for Learning Signed Network Embedding

2019 · Frontiers in Big Data

Network embedding, which aims at learning distributed representations for nodes in networks, is a critical task with wide downstream applications. Most existing studies focus on networks with a single type of edges, whereas in many …

TGNN: A Joint Semi-supervised Framework for Graph-level Classification

2022

This paper studies semi-supervised graph classification, a crucial task with a wide range of applications in social network analysis and bioinformatics. Recent works typically adopt graph neural networks to learn graph-level representations for classification, failing …

GraphText: Graph Reasoning in Text Space

2023 · arXiv (Cornell University)

Large Language Models (LLMs) have gained the ability to assimilate human knowledge and facilitate natural language interactions with both humans and other LLMs. However, despite their impressive achievements, LLMs have not made significant advancements in …

CoType

2017

Extracting entities and relations for types of interest from text is important for understanding massive text corpora. Traditionally, systems of entity relation extraction have relied on human-annotated corpora for training and adopted an incremental pipeline. …

CoType: Joint Extraction of Typed Entities and Relations with Knowledge Bases

2016 · arXiv (Cornell University)

Extracting entities and relations for types of interest from text is important for understanding massive text corpora. Traditionally, systems of entity relation extraction have relied on human-annotated corpora for training and adopted an incremental pipeline. …