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Deep Semantic Network Representation
2020 IEEE International Conference on Data Mining (ICDM), 2020Network representation aims to learn low-dimensional vector representations of network nodes while preserving the inherent properties of the network. For all its popularity, majority of the existing methods focus on exploitation of diverse information, including network topology and semantic information on nodes of network, and ignore their implicit ...
Xuexiong Luo +4 more
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Localized Network Representations
2003The talk will concern compact, localized and distributed network representation methods. Traditional approaches to network representation are based on global data structures, which require access to the entire structure even if the sought information involves only a small and local set of entities.
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A representation for the Mexican political networks
Social Networks, 2007Abstract A compressed graph representation for use with the Mexican political networks is introduced. Properties of these graphs are investigated. It is also explained how the Jorge–Schmidt power centrality index can be used to index the centrality of nodes in the original network from the compressed graph representation.
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A Text Network Representation Model
2008 Fifth International Conference on Fuzzy Systems and Knowledge Discovery, 2008Text representation is the basis of text processing. Most current text representation models ignore the words' inter-relations, which result in the loss of textpsilas structure information. This paper proposed a novel text representation model, which uses lexical network to represent the text and retains the text's structure. According to the different
Jianyi Liu, Jinghua Wang, Cong Wang 0003
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2019
One of the fundamental features of the process of representation is extensive interactions among different political actors. Social interactions play a key role in defining actors' preferences and behavior, thereby shaping the course of decision-making and its outcomes. Therefore, understanding the patterns of social interactions among political actors
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One of the fundamental features of the process of representation is extensive interactions among different political actors. Social interactions play a key role in defining actors' preferences and behavior, thereby shaping the course of decision-making and its outcomes. Therefore, understanding the patterns of social interactions among political actors
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Network Representation of the External Ear
The Journal of the Acoustical Society of America, 1972Two of the basic requirements of an ear-model design is that the transfer characteristic of the ear canal and the impedance as measured at the eardrum closely agree with results obtained on real ears. In the case of the electrical analog model, this requires that the number of sections that are used to represent the ear canal provide an upper cutoff ...
M B, Gardner, M S, Hawley
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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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Network pictures: concepts and representations
European Journal of Marketing, 2006PurposeThere has recently been an increase in interest in the notion of “network pictures” amongst researchers in the field of business‐to‐business marketing. Network pictures are managers' subjective mental representations of their relevant business environment.
Henneberg, Stephan +3 more
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Deep networks and network representation in bioinformatics
Methods, 2021Xing-Ming, Zhao, Fang-Xiang, Wu
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Symbolic representation of neural networks
Computer, 1996Neural networks often surpass decision trees in predicting pattern classifications, but their predictions cannot be explained. This algorithm's symbolic representations make each prediction explicit and understandable. Our approach to understanding a neural network uses symbolic rules to represent the network decision process. The algorithm, NeuroRule,
Rudy Setiono, Huan Liu 0001
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