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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...
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Stephen Marshall
Road hierarchy and network structure are intimately linked; however, there is not a consistent basis for representing and analyzing the particular hierarchical nature of road network structure. This paper introduces the line structure?identified mathemat...
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Zhuoming Xu, Hanlin Liu, Jian Li, Qianqian Zhang and Yan Tang
Knowledge graph-based recommendation methods are a hot research topic in the field of recommender systems in recent years. As a mainstream knowledge graph-based recommendation method, the propagation-based recommendation method captures users? potential ...
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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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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...
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Jianfei Li, Yongbin Wang and Zhulin Tao
In recent years, graph neural networks (GNNS) have been demonstrated to be a powerful way to learn graph data. The existing recommender systems based on the implicit factor models mainly use the interactive information between users and items for trainin...
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Yanhao Li and Wei Liu
Event prediction is a knowledge inference problem that predicts the consequences or effects of an event based on existing information. Early work on event prediction typically modeled the event context to predict what would happen next. Moreover, the pre...
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Chun Liu, Yaohui Hu, Zheng Li, Junkui Xu, Zhigang Han and Jianzhong Guo
The classification and recognition of the shapes of buildings in map space play an important role in spatial cognition, cartographic generalization, and map updating. As buildings in map space are often represented as the vector data, research was conduc...
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Kuekyeng Kim, Yuna Hur, Gyeongmin Kim and Heuiseok Lim
In an age overflowing with information, the task of converting unstructured data into structured data are a vital task of great need. Currently, most relation extraction modules are more focused on the extraction of local mention-level relations?usually ...
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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...
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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...
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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...
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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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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...
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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...
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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...
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Emmanouil Krasanakis and Andreas Symeonidis
To help developers discover libraries suited to their software projects, automated approaches often start from already employed libraries and recommend more based on co-occurrence patterns in other projects. The most accurate project?library recommendati...
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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...
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Nicholas J. Car and Timo Homburg
In 2012, the Open Geospatial Consortium published GeoSPARQL defining ?an RDF/OWL ontology for [spatial] information?, ?SPARQL extension functions? for performing spatial operations on RDF data and ?RIF rules? defining entailments to be drawn from graph p...
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Yinglin Wang and Xinyu Xu
Reasoning on temporal knowledge graphs, which aims to infer new facts from existing knowledge, has attracted extensive attention and in-depth research recently. One of the important tasks of reasoning on temporal knowledge graphs is entity prediction, wh...
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