|
|
|
|
|
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...
ver más
|
|
|
|
|
|
|
|
|
|
|
Yuxun Lu and Ryutaro Ichise
Knowledge graph completion (KGC) models are a feasible approach for manipulating facts in knowledge graphs. However, the lack of entity types in current KGC models results in inaccurate link prediction results. Most existing type-aware KGC models require...
ver más
|
|
|
|
|
|
|
|
|
|
|
Lili Sun, Xueyan Liu, Min Zhao and Bo Yang
Variational graph autoencoder, which can encode structural information and attribute information in the graph into low-dimensional representations, has become a powerful method for studying graph-structured data. However, most existing methods based on v...
ver más
|
|
|
|
|
|
|
|
|
|
|
Louis Béthune, Yacouba Kaloga, Pierre Borgnat, Aurélien Garivier and Amaury Habrard
We propose a novel algorithm for unsupervised graph representation learning with attributed graphs. It combines three advantages addressing some current limitations of the literature: (i) The model is inductive: it can embed new graphs without re-trainin...
ver más
|
|
|
|
|
|
|
|
|
|
|
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...
ver más
|
|
|
|
|
|
|
|
|
|
|
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 ...
ver más
|
|
|
|
|
|
|
|
|
|
|
Yifei Wang, Shiyang Chen, Guobin Chen, Ethan Shurberg, Hang Liu and Pengyu Hong
This work considers the task of representation learning on the attributed relational graph (ARG). Both the nodes and edges in an ARG are associated with attributes/features allowing ARGs to encode rich structural information widely observed in real appli...
ver más
|
|
|
|
|
|
|
|
|
|
|
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...
ver más
|
|
|
|
|
|
|
|
|
|
|
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...
ver más
|
|
|
|
|
|
|
|
|
|
|
Lu Zhang, Hongyu Yang and Xiping Wu
Air traffic management (ATM) relies on the running condition of the air traffic control sector (ATCS), and assessing whether it is overloaded is crucial for efficiency and safety for the entire aviation industry. Previous approaches to evaluating air tra...
ver más
|
|
|
|
|
|
|
|
|
|
|
Tianxing Wu, Chaoyu Gao, Lin Li and Yuxiang Wang
In recent years, the scale of knowledge graphs and the number of entities have grown rapidly. Entity matching across different knowledge graphs has become an urgent problem to be solved for knowledge fusion. With the importance of entity matching being i...
ver más
|
|
|
|
|
|
|
|
|
|
|
Zongcai Huang, Peiyuan Qiu, Li Yu and Feng Lu
Geographic relation completion contributes greatly to improving the quality of large-scale geographic knowledge graphs (GeoKGs). However, the internal features of a GeoKG used in large-scale GeoKGs embedding are often limited by the weak connectivity bet...
ver más
|
|
|
|
|
|
|
|
|
|
|
Jiagang Song, Jiayu Song, Xinpan Yuan, Xiao He and Xinghui Zhu
With the rapid development of Internet technology, how to mine and analyze massive amounts of network information to provide users with accurate and fast recommendation information has become a hot and difficult topic of joint research in industry and ac...
ver más
|
|
|
|
|
|
|
|
|
|
|
Yuexuan Zhao and Jing Huang
Graph variational auto-encoder (GVAE) is a model that combines neural networks and Bayes methods, capable of deeper exploring the influential latent features of graph reconstruction. However, several pieces of research based on GVAE employ a plain prior ...
ver más
|
|
|
|
|
|
|
|
|
|
|
Chuangtao Ma, Bálint Molnár and András Benczúr
To tackle the issues of semantic collision and inconsistencies between ontologies and the original data model while learning ontology from relational database (RDB), a semi-automatic semantic consistency checking method based on graph intermediate repres...
ver más
|
|
|
|
|
|
|
|
|
|
|
Shicheng Cheng, Liang Zhang, Bo Jin, Qiang Zhang, Xinjiang Lu, Mao You and Xueqing Tian
Our work is to better discover potential DTI and provide new options for drug redirection.
|
|
|
|
|
|
|
|
|
|
|
Yiran Hao, Yiqiang Sheng and Jinlin Wang
We use the proposed packet2vec learning algorithm for IDS preprocessing, the basic steps of IDS are as follows. First, the originally collected traffic is split into packets to be truncated into fixed length. Next, the packet2vec learning algorithm is us...
ver más
|
|
|
|
|
|
|
|
|
|
|
Wenfei Ji, Tonghai Jiang, Meng Wang, Xinyu Tang, Guang Chen and Shan Yang
Currently, low-dimensional embedded representation learning models are the mainstream approach in knowledge representation research, due to ease of calculation and ability to utilize the spatial relationship between knowledge areas, which benefit from st...
ver más
|
|
|
|
|
|
|
|
|
|
|
Shuli Wang, Xuewen Li, Xiaomeng Kou, Jin Zhang, Shaojie Zheng, Jinlong Wang and Jibing Gong
Predicting users? next behavior through learning users? preferences according to the users? historical behaviors is known as sequential recommendation. In this task, learning sequence representation by modeling the pairwise relationship between items in ...
ver más
|
|
|
|
|
|
|
|
|
|
|
Xinjie Zhao, Shiyun Wang and Hao Wang
This study aims to give an insight into the development trends and patterns of social organizations (SOs) in China from the perspective of network science integrating geography and public policy information embedded in the network structure. Firstly, we ...
ver más
|
|
|
|
|
|