Zobrazeno 1 - 10
of 384
pro vyhledávání: '"Li, Jundong"'
Federated Graph Learning (FGL) is tasked with training machine learning models, such as Graph Neural Networks (GNNs), for multiple clients, each with its own graph data. Existing methods usually assume that each client has both node features and grap
Externí odkaz:
http://arxiv.org/abs/2411.08374
Autor:
He, Yinhan, Zheng, Wendy, Zhu, Yaochen, Ma, Jing, Mishra, Saumitra, Raman, Natraj, Liu, Ninghao, Li, Jundong
Graph Neural Networks (GNNs) have been widely deployed in various real-world applications. However, most GNNs are black-box models that lack explanations. One strategy to explain GNNs is through counterfactual explanation, which aims to find minimum
Externí odkaz:
http://arxiv.org/abs/2410.19978
Autor:
Zhang, Kexin, Liu, Shuhan, Wang, Song, Shi, Weili, Chen, Chen, Li, Pan, Li, Sheng, Li, Jundong, Ding, Kaize
Distribution shifts on graphs -- the discrepancies in data distribution between training and employing a graph machine learning model -- are ubiquitous and often unavoidable in real-world scenarios. These shifts may severely deteriorate model perform
Externí odkaz:
http://arxiv.org/abs/2410.19265
Publikováno v:
EMNLP 2024 (Findings)
In recent years, Graph Neural Networks (GNNs) have become successful in molecular property prediction tasks such as toxicity analysis. However, due to the black-box nature of GNNs, their outputs can be concerning in high-stakes decision-making scenar
Externí odkaz:
http://arxiv.org/abs/2410.15165
Large Language Models (LLMs) have demonstrated remarkable success across a wide range of language tasks, but their deployment on edge devices remains challenging due to the substantial memory requirements imposed by their large parameter sizes. Weigh
Externí odkaz:
http://arxiv.org/abs/2410.13056
Federated Graph Learning (FGL) aims to learn graph learning models over graph data distributed in multiple data owners, which has been applied in various applications such as social recommendation and financial fraud detection. Inherited from generic
Externí odkaz:
http://arxiv.org/abs/2408.09393
Autor:
Zhu, Yaochen, Wu, Liang, Zhang, Binchi, Wang, Song, Guo, Qi, Hong, Liangjie, Simon, Luke, Li, Jundong
Job marketplace is a heterogeneous graph composed of interactions among members (job-seekers), companies, and jobs. Understanding and modeling job marketplace can benefit both job seekers and employers, ultimately contributing to the greater good of
Externí odkaz:
http://arxiv.org/abs/2408.04381
As privacy concerns escalate in the realm of machine learning, data owners now have the option to utilize machine unlearning to remove their data from machine learning models, following recent legislation. To enhance transparency in machine unlearnin
Externí odkaz:
http://arxiv.org/abs/2408.00929
In the field of machine unlearning, certified unlearning has been extensively studied in convex machine learning models due to its high efficiency and strong theoretical guarantees. However, its application to deep neural networks (DNNs), known for t
Externí odkaz:
http://arxiv.org/abs/2408.00920
Graph Neural Networks (GNNs) have been increasingly deployed in a plethora of applications. However, the graph data used for training may contain sensitive personal information of the involved individuals. Once trained, GNNs typically encode such inf
Externí odkaz:
http://arxiv.org/abs/2407.19398