Search Results for author: Edward W Huang

Found 10 papers, 4 papers with code

Unleashing the Power of LLMs as Multi-Modal Encoders for Text and Graph-Structured Data

no code implementations15 Oct 2024 Jiacheng Lin, Kun Qian, Haoyu Han, Nurendra Choudhary, Tianxin Wei, Zhongruo Wang, Sahika Genc, Edward W Huang, Sheng Wang, Karthik Subbian, Danai Koutra, Jimeng Sun

Graph-structured information offers rich contextual information that can enhance language models by providing structured relationships and hierarchies, leading to more expressive embeddings for various applications such as retrieval, question answering, and classification.

Contrastive Learning Data Ablation +3

All Against Some: Efficient Integration of Large Language Models for Message Passing in Graph Neural Networks

no code implementations20 Jul 2024 Ajay Jaiswal, Nurendra Choudhary, Ravinarayana Adkathimar, Muthu P. Alagappan, Gaurush Hiranandani, Ying Ding, Zhangyang Wang, Edward W Huang, Karthik Subbian

In this paper, we investigate how LLMs can be leveraged in a computationally efficient fashion to benefit rich graph-structured data, a modality relatively unexplored in LLM literature.

Graph Learning

Communication-Free Distributed GNN Training with Vertex Cut

no code implementations6 Aug 2023 Kaidi Cao, Rui Deng, Shirley Wu, Edward W Huang, Karthik Subbian, Jure Leskovec

Here, we introduce CoFree-GNN, a novel distributed GNN training framework that significantly speeds up the training process by implementing communication-free training.

Node Classification

You Only Transfer What You Share: Intersection-Induced Graph Transfer Learning for Link Prediction

1 code implementation27 Feb 2023 Wenqing Zheng, Edward W Huang, Nikhil Rao, Zhangyang Wang, Karthik Subbian

We identify this setting as Graph Intersection-induced Transfer Learning (GITL), which is motivated by practical applications in e-commerce or academic co-authorship predictions.

Link Prediction Transfer Learning

Search Behavior Prediction: A Hypergraph Perspective

1 code implementation23 Nov 2022 Yan Han, Edward W Huang, Wenqing Zheng, Nikhil Rao, Zhangyang Wang, Karthik Subbian

With these hyperedges, we augment the original bipartite graph into a new \textit{hypergraph}.

Link Prediction

TuneUp: A Simple Improved Training Strategy for Graph Neural Networks

no code implementations26 Oct 2022 Weihua Hu, Kaidi Cao, Kexin Huang, Edward W Huang, Karthik Subbian, Kenji Kawaguchi, Jure Leskovec

Extensive evaluation of TuneUp on five diverse GNN architectures, three types of prediction tasks, and both transductive and inductive settings shows that TuneUp significantly improves the performance of the base GNN on tail nodes, while often even improving the performance on head nodes.

Data Augmentation

Task-Agnostic Graph Neural Explanations

no code implementations29 Sep 2021 Yaochen Xie, Sumeet Katariya, Xianfeng Tang, Edward W Huang, Nikhil Rao, Karthik Subbian, Shuiwang Ji

TAGE enables the explanation of GNN embedding models without downstream tasks and allows efficient explanation of multitask models.

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