Results 51 to 60 of about 4,728,201 (299)
A Tutorial on Network Embeddings
CoRR, 2018 Network embedding methods aim at learning low-dimensional latent representation of nodes in a network. These representations can be used as features for a wide range of tasks on graphs such as classification, clustering, link prediction, and visualization.
Haochen Chen +3 more
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Multi-Task Network Representation Learning
Frontiers in Neuroscience, 2020 Networks, such as social networks, biochemical networks, and protein-protein interaction networks are ubiquitous in the real world. Network representation learning aims to embed nodes in a network as low-dimensional, dense, real-valued vectors, and ...
Yu Xie +4 more
doaj +1 more source
Greedy routing and virtual coordinates for future networks [PDF]
, 2012 At the core of the Internet, routers are continuously struggling with ever-growing routing and forwarding tables. Although hardware advances do accommodate such a growth, we anticipate new requirements e.g.
Bouabene, Ghazi
core +1 more source
Applied Sciences, 2022 Aiming at the current situation of network embedding research focusing on dynamic homogeneous network embedding and static heterogeneous information network embedding but lack of dynamic information utilization, this paper proposes a dynamic ...
Hualong Bu +3 more
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Attributed Social Network Embedding [PDF]
IEEE Transactions on Knowledge and Data Engineering, 2018 12 pages, 7 ...
Lizi Liao +3 more
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Embedding Wheel - like Networks
Iranian Journal of Mathematical Sciences and Informatics, 2023 Summary: One of the important features of an interconnection network is its ability to efficiently simulate programs or parallel algorithms written for other architectures. Such a simulation problem can be mathematically formulated as a graph embedding problem. In this paper we compute the lower bound for dilation and congestion of embedding onto wheel-
Rajan, R. Sundara +4 more
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Embedded Sensor Networks [PDF]
, 2009 Embedded sensor networks are distributed systems for sensing and in situ processing of spatially and temporally dense data from resource-limited and harsh environments such as seismic zones, ecological contamination sites are battle fields. From an application point of view, many interesting questions arise from sensor network technology that go far ...
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Full-Network Embedding in a Multimodal Embedding Pipeline
CoRR, 2017 This work is partially supported by the Joint Study Agreement no. W156463 under the IBM/BSC Deep Learning Center agreement, by the Spanish Government through Programa Severo Ochoa (SEV-2015- 0493), by the Spanish Ministry of Science and Technology through TIN2015-65316-P project, by the Generalitat de Catalunya (contracts 2014-SGR-1051), and by the ...
Vilalta Arias, Armand +7 more
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Embedding and Trajectories of Temporal Networks
IEEE Access, 2023 Temporal network data are increasingly available in various domains, and often represent highly complex systems with intricate structural and temporal evolutions. Due to the difficulty of processing such complex data, it may be useful to coarse grain temporal network data into a numeric trajectory embedded in a low-dimensional space. We refer to such a
Chanon Thongprayoon +2 more
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From wide to deep: dimension lifting network for parameter-efficient knowledge graph embedding [PDF]
Knowledge graph embedding (KGE) that maps entities and relations into vector representations is essential for downstream applications. Conventional KGE methods require high-dimensional representations to learn the complex structure of knowledge graph ...
Zhang, He +6 more
core +1 more source