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Xin Tian and Yuan Meng
Multi-relational graph neural networks (GNNs) have found widespread application in tasks involving enhancing knowledge representation and knowledge graph (KG) reasoning. However, existing multi-relational GNNs still face limitations in modeling the excha...
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Longxin Yao, Yun Lu, Mingjiang Wang, Yukun Qian and Heng Li
The construction of complex networks from electroencephalography (EEG) proves to be an effective method for representing emotion patterns in affection computing as it offers rich spatiotemporal EEG features associated with brain emotions. In this paper, ...
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Xiu Li, Aron Henriksson, Martin Duneld, Jalal Nouri and Yongchao Wu
Educational content recommendation is a cornerstone of AI-enhanced learning. In particular, to facilitate navigating the diverse learning resources available on learning platforms, methods are needed for automatically linking learning materials, e.g., in...
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Lianlian He, Hao Li and Rui Zhang
Recent advances in knowledge graphs show great promise to link various data together to provide a semantic network. Place is an important part in the big picture of the knowledge graph since it serves as a powerful glue to link any data to its georeferen...
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Binita Kusum Dhamala, Babu R. Dawadi, Pietro Manzoni and Baikuntha Kumar Acharya
Graph representation is recognized as an efficient method for modeling networks, precisely illustrating intricate, dynamic interactions within various entities of networks by representing entities as nodes and their relationships as edges. Leveraging the...
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Vivian W. H. Wong and Kincho H. Law
Crowd congestion is one of the main causes of modern public safety issues such as stampedes. Conventional crowd congestion monitoring using closed-circuit television (CCTV) video surveillance relies on manual observation, which is tedious and often error...
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Sirui Shen, Daobin Zhang, Shuchao Li, Pengcheng Dong, Qing Liu, Xiaoyu Li and Zequn Zhang
Heterogeneous graph neural networks (HGNNs) deliver the powerful capability to model many complex systems in real-world scenarios by embedding rich structural and semantic information of a heterogeneous graph into low-dimensional representations. However...
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Huansha Wang, Qinrang Liu, Ruiyang Huang and Jianpeng Zhang
Multi-modal entity alignment refers to identifying equivalent entities between two different multi-modal knowledge graphs that consist of multi-modal information such as structural triples and descriptive images. Most previous multi-modal entity alignmen...
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Ji Zhang, Xiangze Jia, Zhen Wang, Yonglong Luo, Fulong Chen, Gaoming Yang and Lihui Zhao
Skeleton-based action recognition depends on skeleton sequences to detect categories of human actions. In skeleton-based action recognition, the recognition of action scenes with more than one subject is named as interaction recognition. Different from t...
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Aleksandar Ivanovski, Milos Jovanovik, Riste Stojanov and Dimitar Trajanov
In this work, we present a state-of-the-art solution for automatic playlist continuation through a knowledge graph-based recommender system. By integrating representational learning with graph neural networks and fusing multiple data streams, the system ...
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