Results 1 to 10 of about 206,969 (259)
Research on traffic representation in network anomaly detection
Tongxin xuebaoAiming to address the problem of information loss in traffic representation for network anomaly detection, the impact of feature information dimension of different traffic representation on anomaly detection performance was analyzed from the perspective ...
SUN Jianwen, ZHANG Bin, CHANG Heyu
doaj
Learning Compact Representations of Constraint Networks
Passive constraint acquisition aims to learn constraint networks from examples of solutions and non-solutions. There typically exist many constraint networks that are consistent with a given set of examples, so the performance of an acquisition system is critically dependent on its ability to determine which network will generalize the best to unseen ...
Christian Bessiere +2 more
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Graph Representation Learning for Social Networks.
, 2021 Online social networks provide a rich source of information about millions of users worldwide. However, due to sparsity and complex structure, analyzing these networks is quite challenging and expensive. Recently, graph embedding emerged to map networked data into low-dimensional representations, i.e. vector embeddings.
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Representation and learning in feedforward neural networks
CWI Quarterly, 1993 Summary: The paper gives an introduction to feedforward neural networks. The aim is to present some of the basics of artificial neural networks, with a particular emphasis on the following two central issues. The first central issue of this paper is: in what sense do artificial neural networks represent mathematical functions, and what mathematical ...
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A survey on heterogeneous network representation learning
Pattern Recognition, 2021Abstract Heterogeneous information networks usually contain different kinds of nodes and distinguishing types of relations, which can preserve more information than homogeneous information networks. Heterogeneous network representation learning attempts to learn a low-dimensional representation for each node and capture rich semantic information of ...
Yu Xie 0009 +5 more
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Recipe Representation Learning with Networks
Proceedings of the 30th ACM International Conference on Information & Knowledge Management, 2021Learning effective representations for recipes is essential in food studies for recommendation, classification, and other applications. Unlike what has been developed for learning textual or cross-modal embeddings for recipes, the structural relationship among recipes and food items are less explored.
Yijun Tian 0001 +3 more
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Contrastive representation learning on dynamic networks
Neural Networks, 2023Representation learning for dynamic networks is designed to learn the low-dimensional embeddings of nodes that can well preserve the snapshot structure, properties and temporal evolution of dynamic networks. However, current dynamic network representation learning methods tend to focus on estimating or generating observed snapshot structures, paying ...
Pengfei Jiao +6 more
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On Representation Learning for Road Networks
ACM Transactions on Intelligent Systems and Technology, 2020Informative representation of road networks is essential to a wide variety of applications on intelligent transportation systems. In this article, we design a new learning framework, called Representation Learning for Road Networks (RLRN), which explores various intrinsic properties of road networks to learn embeddings of intersections and
Mengxiang Wang +3 more
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Learning IP network representations
ACM SIGCOMM Computer Communication Review, 2019We present DIP, a deep learning based framework to learn structural properties of the Internet, such as node clustering or distance between nodes. Existing embedding-based approaches use linear algorithms on a single source of data, such as latency or hop count information, to approximate the position of a node in the Internet.
Mingda Li +3 more
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Learning Network Representation Through Reinforcement Learning
ICASSP 2020 - 2020 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP), 2020Network Representation Learning embeds each node in a network into a low-dimensional real-value vector which can be used for downstream tasks such as link prediction and recommendation. Many existing approaches use unsupervised or (semi-)supervised methods to explore the network topology and learn representations from it.
Siqi Shen +6 more
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