Zobrazeno 1 - 10
of 640
pro vyhledávání: '"Pan, Shirui"'
Autor:
Shi, Guangsi, Deng, Xiaofeng, Luo, Linhao, Xia, Lijuan, Bao, Lei, Ye, Bei, Du, Fei, Pan, Shirui, Li, Yuxiao
Recommender systems are pivotal in enhancing user experiences across various web applications by analyzing the complicated relationships between users and items. Knowledge graphs(KGs) have been widely used to enhance the performance of recommender sy
Externí odkaz:
http://arxiv.org/abs/2406.15859
Autor:
Wang, Yili, Liu, Yixin, Shen, Xu, Li, Chenyu, Ding, Kaize, Miao, Rui, Wang, Ying, Pan, Shirui, Wang, Xin
To build safe and reliable graph machine learning systems, unsupervised graph-level anomaly detection (GLAD) and unsupervised graph-level out-of-distribution (OOD) detection (GLOD) have received significant attention in recent years. Though those two
Externí odkaz:
http://arxiv.org/abs/2406.15523
Autor:
Wang, Kun, Zhang, Guibin, Zhang, Xinnan, Fang, Junfeng, Wu, Xun, Li, Guohao, Pan, Shirui, Huang, Wei, Liang, Yuxuan
Graph Neural Networks (GNNs) have become pivotal tools for a range of graph-based learning tasks. Notably, most current GNN architectures operate under the assumption of homophily, whether explicitly or implicitly. While this underlying assumption is
Externí odkaz:
http://arxiv.org/abs/2406.12539
In recent years, there has been notable interest in investigating combinatorial optimization (CO) problems by neural-based framework. An emerging strategy to tackle these challenging problems involves the adoption of graph neural networks (GNNs) as a
Externí odkaz:
http://arxiv.org/abs/2406.03647
The vision-and-language navigation (VLN) task necessitates an agent to perceive the surroundings, follow natural language instructions, and act in photo-realistic unseen environments. Most of the existing methods employ the entire image or object fea
Externí odkaz:
http://arxiv.org/abs/2406.01256
Graph Neural Networks (GNNs) have gained significant attention as a powerful modeling and inference method, especially for homophilic graph-structured data. To empower GNNs in heterophilic graphs, where adjacent nodes exhibit dissimilar labels or fea
Externí odkaz:
http://arxiv.org/abs/2405.20652
Autor:
Zhang, Zhaoxi, Zhang, Xiaomei, Zhang, Yanjun, Zhang, Leo Yu, Chen, Chao, Hu, Shengshan, Gill, Asif, Pan, Shirui
The Large Language Model (LLM) watermark is a newly emerging technique that shows promise in addressing concerns surrounding LLM copyright, monitoring AI-generated text, and preventing its misuse. The LLM watermark scheme commonly includes generating
Externí odkaz:
http://arxiv.org/abs/2405.19677
Social recommendation models weave social interactions into their design to provide uniquely personalized recommendation results for users. However, social networks not only amplify the popularity bias in recommendation models, resulting in more freq
Externí odkaz:
http://arxiv.org/abs/2405.16772
Graph anomaly detection (GAD), which aims to identify abnormal nodes that differ from the majority within a graph, has garnered significant attention. However, current GAD methods necessitate training specific to each dataset, resulting in high train
Externí odkaz:
http://arxiv.org/abs/2405.16771
Autor:
Wang, Jiapu, Sun, Kai, Luo, Linhao, Wei, Wei, Hu, Yongli, Liew, Alan Wee-Chung, Pan, Shirui, Yin, Baocai
Temporal Knowledge Graph Reasoning (TKGR) is the process of utilizing temporal information to capture complex relations within a Temporal Knowledge Graph (TKG) to infer new knowledge. Conventional methods in TKGR typically depend on deep learning alg
Externí odkaz:
http://arxiv.org/abs/2405.14170