|
|
|
|
|
Sanaz Gheibi, Tania Banerjee, Sanjay Ranka and Sartaj Sahni
This paper proposes a new time-respecting graph (TRG) representation for contact sequence temporal graphs. Our representation is more memory-efficient than previously proposed representations and has run-time advantages over the ordered sequence of edges...
ver más
|
|
|
|
|
|
|
|
|
|
|
Xinya Lei, Yuewei Wang, Wei Han and Weijing Song
Coastal cities are increasingly vulnerable to urban storm surge hazards and the secondary hazards they cause (e.g., coastal flooding). Accurate representation of the spatio-temporal process of hazard event development is essential for effective emergency...
ver más
|
|
|
|
|
|
|
|
|
|
|
Guangsheng Dong, Rui Li, Fa Li, Zhaohui Liu, Huayi Wu, Longgang Xiang, Wensen Yu, Jie Jiang, Hongping Zhang and Fangning Li
An imbalance in urban development in China has become a contradiction. Points of Interest (POIs) serve as representations of the spatial distribution of urban functions. Analyzing POI spatial co-occurrence patterns can reveal the agglomeration patterns o...
ver más
|
|
|
|
|
|
|
|
|
|
|
Kexiang Qian, Hongyu Yang, Ruyu Li, Weizhe Chen, Xi Luo and Lihua Yin
With the rapid growth of IoT devices, the threat of botnets is becoming increasingly worrying. There are more and more intelligent detection solutions for botnets that have been proposed with the development of artificial intelligence. However, due to th...
ver más
|
|
|
|
|
|
|
|
|
|
|
Zhenping Li, Zhen Cao, Pengfei Li, Yong Zhong and Shaobo Li
The task of multi-hop question generation (QG) seeks to generate questions that require a complex reasoning process that spans multiple sentences and answers. Beyond the conventional challenges of what to ask and how to ask, multi-hop QG necessitates sop...
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
|
|
|
|
|
|
|
|
|
|
|
Jianjun Wu, Yuxue Hu, Zhongqiang Huang, Junsong Li, Xiang Li and Ying Sha
Link prediction is a critical prerequisite and foundation task for social network security that involves predicting the potential relationship between nodes within a network or graph. Although the existing methods show promising performance, they often i...
ver más
|
|
|
|
|
|
|
|
|
|
|
Ying Liu, Peng Wang and Di Yang
Knowledge graph embedding learning aims to represent the entities and relationships of real-world knowledge as low-dimensional dense vectors. Existing knowledge representation learning methods mostly aggregate only the internal information of triplets an...
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
|
|
|
|
|
|
|
|
|
|
|
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
|
|
|
|
|
|