no code implementations • 15 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.
1 code implementation • 23 Jul 2024 • Sabyasachi Basu, Daniel Paul-Pena, Kun Qian, C. Seshadhri, Edward W Huang, Karthik Subbian
A fundamental task in graph mining is to discover these dense subgraphs.
no code implementations • 20 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.
no code implementations • 1 Mar 2024 • Nurendra Choudhary, Edward W Huang, Karthik Subbian, Chandan K. Reddy
This lack of interpretability hinders the development and adoption of new techniques in the field.
no code implementations • 6 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.
Ranked #2 on Node Classification on Reddit
1 code implementation • 27 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.
1 code implementation • 23 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}.
no code implementations • 26 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.
2 code implementations • ICLR 2022 • Wenqing Zheng, Edward W Huang, Nikhil Rao, Sumeet Katariya, Zhangyang Wang, Karthik Subbian
We propose Cold Brew, a teacher-student distillation approach to address the SCS and noisy-neighbor challenges for GNNs.
no code implementations • 29 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.