By representing objects expressed as graphs into the vector space , this technique makes it possible to use point matching algorithms as opposed to costly graph matching approaches . In this paper , representatives of object classes in the vector space is obtained through graph embedding . To classify a query , instead of using exhaustive search , a more effective way of comparing it to class representatives is employed . Such shilling behaviors are generally termed group shilling attacks , which are more harmful to collaborative recommender systems than traditional shilling attacks . In this paper , we propose a graph embedding-based method to detect group shilling attacks in collaborative recommender systems . First , we construct a user relationship graph by analyzing the user rating behaviors and use a graph embedding method to obtain the low-dimensional vector representation of each node in the user relationship graph . Recently , graph embedding-based methods have been developed in dimensionality reduction ( DR ) and classification of hyperspectral image ( HSI ) . The key step for graph embedding methods is the construction of graph . Manifold learning techniques have been demonstrated to be successful in representing spectral signatures in hyperspectral images , which consist of spectral features with very subtle differences and often spatially induced disjoint classes whose neighborhood relations are difficult to capture using traditional graph based embedding techniques . Robust parameter estimation is a challenge in traditional kernel functions that compute neighborhood graphs e.g finding the optimal number of nearest neighbors . Such a low - dimensional embedding is very useful in a variety of applications such as visualization , node classification , link prediction , and recommendation . Various methods of graph embedding have been proposed in the machine learning literature ( e.g. , ) . They generally perform well on smaller networks . Network embedding is increasingly employed to assist network analysis as it is effective to learn latent features that encode linkage information . Various network embedding methods have been proposed , but they are only designed for a single network scenario . However , how to enhance network embedding itself in terms of both efficiency and scalability is still an open problem . We are still short of efficient and scalable network embedding techniques to scale themselves on big graphs with millions or billions of vertices and edges , with the awareness of the intrinsic global and local characteristics of graph data . Most network embedding techniques exploit shallow-structured architectures , and thus lead to sub-optimal network representations . Multiple networks provide rich information for inferring the function of genes or proteins . To extract functional patterns of genes based on multiple heterogeneous networks , network embedding-based methods , aiming to capture non-linear and low-dimensional feature representation based on network biology , have recently achieved remarkable performance in gene function prediction . However , existing methods mainly do not consider the shared information among different networks during the feature learning process . However , most of the existing principles of network embedding do not incorporate auxiliary information such as content and labels of nodes flexibly . In this paper , we take a matrix factorization perspective of network embedding , and incorporate structure , content and label information of the network simultaneously . For structure , we validate that the matrix we construct preserves high-order proximities of the network . However , most of the existing principles of network embedding do not incorporate auxiliary information such as content and labels of nodes flexibly . In this paper , we take a matrix factorization perspective of network embedding , and incorporate structure , content and label information of the network simultaneously . For structure , we validate that the matrix we construct preserves high-order proximities of the network . Nevertheless , due to the lack of rich contexts in social media , the performance of these embedding based topic models still remain unsatisfactory . In order to enrich the contexts of keywords , we propose two network based embedding methods , both of which can generate rich contexts for keywords by random walks and produce coherent keyword embeddings for topic modeling . Besides , processing continuous spatio-temporal information in social media is also very challenging . In current reconfigurable architectures , redundant PEs are utilized for fault tolerance . In the presence of faulty PEs , the physical topologies of various chips may be different , so the concept of virtual topology from network embedding problem has been used to alleviate the burden for the operating systems . With limited hardware resources , how to reconfigure a system into the most effective virtual topology such that the maximum repair rate can be reached presents a significant challenge . Our proposed connectome network achieves an overall recognition accuracy of 80.20 % and 82.05 % in binary classification of separating high versus low scoring subjects on tasks of Taiwanese Face Memory Test ( TFMT ) and component inverse efficiency score ( cIE ) respectively . Further , our network embedding representation demonstrate distinct connectivity patterns in key face processing brain regions ( ROIs ) when comparing between high and low face processing and memory ability . Most existing graph embedding methods do not scale for real world information networks which usually contain millions of nodes . In this paper , we propose a novel network embedding method called the “ LINE , ” which is suitable for arbitrary types of information networks : undirected , directed , and / or weighted . The method optimizes a carefully designed objective function that preserves both the local and global network structures . We anticipate that a new model with a carefully designed objective function that preserves properties of the graph and an efficient optimization technique should effectively find the embedding of millions of nodes . In this paper , we propose such a network embedding model called the “ LINE , ” which is able to scale to very large , arbitrary types of networks : undirected , directed and / or weighted . The model optimizes an objective which preserves both the local and global network structures . The high-dimensional urban human activity data is embedded into the low-dimensional space ( feature space ) using the node2vec method . Compared to the traditional method of directly using high dimensional data analysis , node2vec can better detect the spatial relationship and structure of the network which are important for us to understand the urban functional areas more clearly . Based on Shanghai taxi data and subway travel data , we focused on the distribution and characteristics of functional areas in Shanghai . DeepWalk is the most classical network representation learning algorithm , which samples the next hop nodes of the walker with an equal probability method through the random walk strategy . Node2vec improves the random walk procedures , thus improving the performance of node2vec algorithm on various tasks . Therefore , we propose an improved DeepWalk algorithm based on preference random walk ( PDW ) , which modifies the single undirected edge into two one-way directed edges in the network , and then gives each one-way directed edge a walk probability based on local random walk algorithm . This paper describes the creation of PT-LKB , new Portuguese word embeddings learned from a large lexical-semantic knowledge base ( LKB ) , using the node2vec method . Resulting embeddings combine the strengths of word vector representations and , even with lower dimensions , achieve high scores in genuine similarity , which so far were obtained by exploiting the graph structure of LKBs . However , traditional recommendation methods are challenged by data sparsity and efficiency , as the numbers of users , items , and interactions between the two in many real-world applications increase fast . In this paper , we propose a novel clustering recommender system based on node2vec technology and rich information network , namely , N2VSCDNNR , to solve these challenges . In particular , we use a bipartite network to construct the user – item network and represent the interactions among users ( or items ) by the corresponding one-mode projection network . In particular , we demonstrate how the cover relation of a concept lattice can be retrieved from a computational feasible embedding . Secondly , we show an enhancement for the classical node2vec approach in low dimension . For both directions the overall constraint of FCA of explainable results is preserved . We take for granted the existing semantic relations , for training/test dataset , gathered and validated by crowdsourcing . We describe how all of the mentioned ideas can be followed after using the node2vec approach in the feature extraction phase . We show how this approach can lead to acceptable results . Since the invention of word2vec , the skip-gram model has significantly advanced the research of network embedding , such as the recent emergence of the DeepWalk , LINE , PTE , and node2vec approaches . In this work , we show that all of the aforementioned models with negative sampling can be unified into the matrix factorization framework with closed forms . This paper presents a sequence to sequence ( seq2seq ) dependency parser by directly predicting the relative position of head for each given word , which therefore results in a truly end-to-end seq2seq dependency parser for the first time . Enjoying the advantage of seq2seq modeling , we enrich a series of embedding enhancement , including firstly introduced subword and node2vec augmentation . Meanwhile , we propose a beam search decoder with tree constraint and subroot decomposition over the sequence to furthermore enhance our seq2seq parser . We found that the main difference between laundering and regular transactions lies in their output values and neighbourhood information . Then , we propose and evaluate a set of classifiers based on four types of graph features : immediate neighbours , curated features , deepwalk embeddings , and node2vec embeddings to classify money laundering and regular transactions . Results show that the node2vec-based classifier outperforms other classifiers in binary classification reaching an average accuracy of 92.29 % and an F1-measure of 0.93 and high robustness over a 2.5-year time span . Deep reinforcement learning ( RL ) is a powerful tool at learning policies for these nonlinear , complex processes in high-dimension . To control an epidemic outbreak on a Susceptible-Infected-Susceptible network epidemic model , we design a RL framework with a custom reward structure using the node2vec embedding technique . Results indicate deep RL is able to determine and converge on an optimal intervention policy in a relatively short time . The existing graph embedding methods are designed for static networks and they can not capture evolving patterns in a large dynamic network . In this paper , we propose a dynamic embedding method , dynnode2vec , based on the well-known graph embedding method node2vec . Node2vec is a random walk based embedding method for static networks . DeepWalk perozzi2014deepwalk learns embeddings via the prediction of the local neighborhood of nodes , sampled from random walks on the graph . LINE tang2015line and node2vec grovernode2vec extend DeepWalk with more sophisticated random walk or breadth - first search schemes . For all these methods , however , a multi - step pipeline including random walk generation and semi - supervised training is required where each step has to be optimized separately . document : node2vec : Scalable Feature Learning for Networks Prediction tasks over nodes and edges in networks require careful effort in engineering features used by learning algorithms . The receivers are capable of accumulating rateless information pieces from different senders and jointly decoding the packet , largely improving throughput . We develop a practical protocol , called HOPE , which instantiates the hopless networking paradigm . Compared with the existing opportunistic routing protocol family , HOPE best exploits the wireless channel diversity and takes full advantage of the wireless broadcast effect . HOPE ( Heritage of the People ’s Europe ) is a “ Best Practice Network " for archives , libraries , museums and institutions operating in the fields of social and union history . The project provides unified access to materials about the European social and labour history from the 18th to 21st centuries . In this paper , we propose a novel model for high-dimensional data , called the Hybrid Orthogonal Projection and Estimation ( HOPE ) model , which combines a linear orthogonal projection and a finite mixture model under a unified generative modeling framework . The HOPE model itself can be learned unsupervised from unlabelled data based on the maximum likelihood estimation as well as discriminatively from labelled data . More interestingly , we have shown the proposed HOPE models are closely related to neural networks ( NNs ) in a sense that each hidden layer can be reformulated as a HOPE model . The CDP requires desirable elements ( ideally , of indigenous origin ) to be specified for the four HOPE structural components . Hualish has an enviable trove of elements that can be used to structure its redesign . These cultural elements will form the building blocks for the four HOPE structural components . Many of these elements contribute to multiple HOPE components ; thus , instead of being organized by structural component , they are broadly described here as belief systems , social interaction norms , and rituals . Importantly , their existence does not imply that they have been adopted appropriately and by large number of Hualish members . The HOPE programming model is presented , using both an informal description of its primitives , and a definition of its formal semantics . We then describe the abstract algorithms for implementing HOPE in a distributed environment , as well as the prototype HOPE system that embeds the HOPE primitives in C using the PVM message passing library . Finally , we introduce the performance characteristics of the prototype HOPE system , and describe future research directions . 2 The Approach and the HOPE Framework The approach . We propose the High-level Objective-based Policy for Enterprises ( HOPE ) framework which is aimed at addressing the above research problems via policy-based design and management . As presented in Figure 1 , with HOPE , we specify quality requirements ( which are driven by original business rules and regulations ) in the form of business-level policies which will then be formulated as quality objectives applicable on business entities . Also in SPM , it recommends items , regardless of whether user has purchased that item before or not . This thesis proposes an E-commerce recommendation system , SEERs ( Stacking Ensemble E-commerce Recommendation system ) , which improves on HOPE system to make better recommendations in the following two ways : i ) Learning the best minimum support for SPA , best k similar users for CF and the best weights for integrating the four used matrices . ii ) Separating their two intermediate matrices of CFPP and SPAPP into four intermediate matrices of CF_notpurchased , SPM_purchased , SPM_notpurchased and purchasehistory matrix which are obtained and merged with the better-learned parameters from ( i ) above . In order to select the appropriate protocol , each protocolpsilas performance should be compared across realistic missions with different traffic profiles , terrain , and atmospheric conditions . The HOPE tool is a framework that combines terrain maps , simulation models of communication protocols , and link budget calculations . This framework allows communication protocol models from various simulations to be visualized on one user interface . Since these nodes have varying capabilities , services running atop are also faced with profound performance unpredictability . In this paper , we propose a Heterogeneous netwOrk Policy Enforcement ( HOPE ) scheme to overcome these challenges . HOPE guarantees that network functions ( NFs ) that implement a policy chain are optimally placed onto heterogeneous NFBs such that the network cost of the policy is minimized . Second , given a certain order proximity , the existing methods can not guarantee accuracy and efficiency simultaneously . To address these challenges , we propose AROPE ( arbitrary-order proximity preserved embedding ) , a novel network embedding method based on SVD framework . We theoretically prove the eigen-decomposition reweighting theorem , revealing the intrinsic relationship between proximities of different orders . The usefulness of NEMII is further validated by case studies . The studies demonstrate the great potential of network embedding method for the miRNA-disease association prediction , and SDNE outperforms other popular network embedding methods : DeepWalk , High-Order Proximity preserved Embedding ( HOPE ) and Laplacian Eigenmaps (LE).ConclusionWe propose a new method , named NEMII , for predicting miRNA-disease associations , which has great potential to benefit the field of miRNA-disease association prediction . Higher-order proximity preserved network embedding has attracted increasing attention recently . In particular , due to the superior scalability , random-walk based network embedding has also been well developed , which could efficiently explore higher-order neighborhood via multi-hop random walks . In these applications , the goal is to find a sparsest Steiner k-TC-spanner of a poset G for a given k and G. The focus of this paper is the relationship between the dimension of a poset and the size of its sparsest Steiner TC-spanner . The dimension of a poset G is the smallest d such that G can be embedded into a d-dimensional directed hypergrid via an order-preserving embedding . We present a nearly tight lower bound on the size of Steiner 2-TC-spanners of d- dimensional directed hypergrids . Specifically , we find that MNMF , a community preserving embedding method , is the most competitive method for the link prediction task . While NetMF is the most competitive baseline for node classification . Third , no single method completely outperforms other embedding methods on both node classification and link prediction tasks . We further provide the theoretical connections between skip-gram based network embedding algorithms and the theory of graph Laplacian . Finally , we present the NetMF method as well as its approximation algorithm for computing network embedding . Our method offers significant improvements over DeepWalk and LINE for conventional network mining tasks .
By representing objects expressed as graphs into the vector space , this technique makes it possible to use point matching algorithms as opposed to costly graph matching approaches . In this paper , representatives of object classes in the vector space is obtained through graph embedding . To classify a query , instead of using exhaustive search , a more effective way of comparing it to class representatives is employed .
Such shilling behaviors are generally termed group shilling attacks , which are more harmful to collaborative recommender systems than traditional shilling attacks . In this paper , we propose a graph embedding-based method to detect group shilling attacks in collaborative recommender systems . First , we construct a user relationship graph by analyzing the user rating behaviors and use a graph embedding method to obtain the low-dimensional vector representation of each node in the user relationship graph .
Recently , graph embedding-based methods have been developed in dimensionality reduction ( DR ) and classification of hyperspectral image ( HSI ) . The key step for graph embedding methods is the construction of graph .
Manifold learning techniques have been demonstrated to be successful in representing spectral signatures in hyperspectral images , which consist of spectral features with very subtle differences and often spatially induced disjoint classes whose neighborhood relations are difficult to capture using traditional graph based embedding techniques . Robust parameter estimation is a challenge in traditional kernel functions that compute neighborhood graphs e.g finding the optimal number of nearest neighbors .
Such a low - dimensional embedding is very useful in a variety of applications such as visualization , node classification , link prediction , and recommendation . Various methods of graph embedding have been proposed in the machine learning literature ( e.g. , ) . They generally perform well on smaller networks .
Network embedding is increasingly employed to assist network analysis as it is effective to learn latent features that encode linkage information . Various network embedding methods have been proposed , but they are only designed for a single network scenario .
However , how to enhance network embedding itself in terms of both efficiency and scalability is still an open problem . We are still short of efficient and scalable network embedding techniques to scale themselves on big graphs with millions or billions of vertices and edges , with the awareness of the intrinsic global and local characteristics of graph data . Most network embedding techniques exploit shallow-structured architectures , and thus lead to sub-optimal network representations .
Multiple networks provide rich information for inferring the function of genes or proteins . To extract functional patterns of genes based on multiple heterogeneous networks , network embedding-based methods , aiming to capture non-linear and low-dimensional feature representation based on network biology , have recently achieved remarkable performance in gene function prediction . However , existing methods mainly do not consider the shared information among different networks during the feature learning process .
However , most of the existing principles of network embedding do not incorporate auxiliary information such as content and labels of nodes flexibly . In this paper , we take a matrix factorization perspective of network embedding , and incorporate structure , content and label information of the network simultaneously . For structure , we validate that the matrix we construct preserves high-order proximities of the network .
However , most of the existing principles of network embedding do not incorporate auxiliary information such as content and labels of nodes flexibly . In this paper , we take a matrix factorization perspective of network embedding , and incorporate structure , content and label information of the network simultaneously . For structure , we validate that the matrix we construct preserves high-order proximities of the network .
Nevertheless , due to the lack of rich contexts in social media , the performance of these embedding based topic models still remain unsatisfactory . In order to enrich the contexts of keywords , we propose two network based embedding methods , both of which can generate rich contexts for keywords by random walks and produce coherent keyword embeddings for topic modeling . Besides , processing continuous spatio-temporal information in social media is also very challenging .
In current reconfigurable architectures , redundant PEs are utilized for fault tolerance . In the presence of faulty PEs , the physical topologies of various chips may be different , so the concept of virtual topology from network embedding problem has been used to alleviate the burden for the operating systems . With limited hardware resources , how to reconfigure a system into the most effective virtual topology such that the maximum repair rate can be reached presents a significant challenge .
Our proposed connectome network achieves an overall recognition accuracy of 80.20 % and 82.05 % in binary classification of separating high versus low scoring subjects on tasks of Taiwanese Face Memory Test ( TFMT ) and component inverse efficiency score ( cIE ) respectively . Further , our network embedding representation demonstrate distinct connectivity patterns in key face processing brain regions ( ROIs ) when comparing between high and low face processing and memory ability .
Most existing graph embedding methods do not scale for real world information networks which usually contain millions of nodes . In this paper , we propose a novel network embedding method called the “ LINE , ” which is suitable for arbitrary types of information networks : undirected , directed , and / or weighted . The method optimizes a carefully designed objective function that preserves both the local and global network structures .
We anticipate that a new model with a carefully designed objective function that preserves properties of the graph and an efficient optimization technique should effectively find the embedding of millions of nodes . In this paper , we propose such a network embedding model called the “ LINE , ” which is able to scale to very large , arbitrary types of networks : undirected , directed and / or weighted . The model optimizes an objective which preserves both the local and global network structures .
The high-dimensional urban human activity data is embedded into the low-dimensional space ( feature space ) using the node2vec method . Compared to the traditional method of directly using high dimensional data analysis , node2vec can better detect the spatial relationship and structure of the network which are important for us to understand the urban functional areas more clearly . Based on Shanghai taxi data and subway travel data , we focused on the distribution and characteristics of functional areas in Shanghai .
DeepWalk is the most classical network representation learning algorithm , which samples the next hop nodes of the walker with an equal probability method through the random walk strategy . Node2vec improves the random walk procedures , thus improving the performance of node2vec algorithm on various tasks . Therefore , we propose an improved DeepWalk algorithm based on preference random walk ( PDW ) , which modifies the single undirected edge into two one-way directed edges in the network , and then gives each one-way directed edge a walk probability based on local random walk algorithm .
This paper describes the creation of PT-LKB , new Portuguese word embeddings learned from a large lexical-semantic knowledge base ( LKB ) , using the node2vec method . Resulting embeddings combine the strengths of word vector representations and , even with lower dimensions , achieve high scores in genuine similarity , which so far were obtained by exploiting the graph structure of LKBs .
However , traditional recommendation methods are challenged by data sparsity and efficiency , as the numbers of users , items , and interactions between the two in many real-world applications increase fast . In this paper , we propose a novel clustering recommender system based on node2vec technology and rich information network , namely , N2VSCDNNR , to solve these challenges . In particular , we use a bipartite network to construct the user – item network and represent the interactions among users ( or items ) by the corresponding one-mode projection network .
In particular , we demonstrate how the cover relation of a concept lattice can be retrieved from a computational feasible embedding . Secondly , we show an enhancement for the classical node2vec approach in low dimension . For both directions the overall constraint of FCA of explainable results is preserved .
We take for granted the existing semantic relations , for training/test dataset , gathered and validated by crowdsourcing . We describe how all of the mentioned ideas can be followed after using the node2vec approach in the feature extraction phase . We show how this approach can lead to acceptable results .
Since the invention of word2vec , the skip-gram model has significantly advanced the research of network embedding , such as the recent emergence of the DeepWalk , LINE , PTE , and node2vec approaches . In this work , we show that all of the aforementioned models with negative sampling can be unified into the matrix factorization framework with closed forms .
This paper presents a sequence to sequence ( seq2seq ) dependency parser by directly predicting the relative position of head for each given word , which therefore results in a truly end-to-end seq2seq dependency parser for the first time . Enjoying the advantage of seq2seq modeling , we enrich a series of embedding enhancement , including firstly introduced subword and node2vec augmentation . Meanwhile , we propose a beam search decoder with tree constraint and subroot decomposition over the sequence to furthermore enhance our seq2seq parser .
We found that the main difference between laundering and regular transactions lies in their output values and neighbourhood information . Then , we propose and evaluate a set of classifiers based on four types of graph features : immediate neighbours , curated features , deepwalk embeddings , and node2vec embeddings to classify money laundering and regular transactions . Results show that the node2vec-based classifier outperforms other classifiers in binary classification reaching an average accuracy of 92.29 % and an F1-measure of 0.93 and high robustness over a 2.5-year time span .
Deep reinforcement learning ( RL ) is a powerful tool at learning policies for these nonlinear , complex processes in high-dimension . To control an epidemic outbreak on a Susceptible-Infected-Susceptible network epidemic model , we design a RL framework with a custom reward structure using the node2vec embedding technique . Results indicate deep RL is able to determine and converge on an optimal intervention policy in a relatively short time .
The existing graph embedding methods are designed for static networks and they can not capture evolving patterns in a large dynamic network . In this paper , we propose a dynamic embedding method , dynnode2vec , based on the well-known graph embedding method node2vec . Node2vec is a random walk based embedding method for static networks .
DeepWalk perozzi2014deepwalk learns embeddings via the prediction of the local neighborhood of nodes , sampled from random walks on the graph . LINE tang2015line and node2vec grovernode2vec extend DeepWalk with more sophisticated random walk or breadth - first search schemes . For all these methods , however , a multi - step pipeline including random walk generation and semi - supervised training is required where each step has to be optimized separately .
document : node2vec : Scalable Feature Learning for Networks Prediction tasks over nodes and edges in networks require careful effort in engineering features used by learning algorithms .
The receivers are capable of accumulating rateless information pieces from different senders and jointly decoding the packet , largely improving throughput . We develop a practical protocol , called HOPE , which instantiates the hopless networking paradigm . Compared with the existing opportunistic routing protocol family , HOPE best exploits the wireless channel diversity and takes full advantage of the wireless broadcast effect .
HOPE ( Heritage of the People ’s Europe ) is a “ Best Practice Network " for archives , libraries , museums and institutions operating in the fields of social and union history . The project provides unified access to materials about the European social and labour history from the 18th to 21st centuries .
In this paper , we propose a novel model for high-dimensional data , called the Hybrid Orthogonal Projection and Estimation ( HOPE ) model , which combines a linear orthogonal projection and a finite mixture model under a unified generative modeling framework . The HOPE model itself can be learned unsupervised from unlabelled data based on the maximum likelihood estimation as well as discriminatively from labelled data . More interestingly , we have shown the proposed HOPE models are closely related to neural networks ( NNs ) in a sense that each hidden layer can be reformulated as a HOPE model .
The CDP requires desirable elements ( ideally , of indigenous origin ) to be specified for the four HOPE structural components . Hualish has an enviable trove of elements that can be used to structure its redesign .
These cultural elements will form the building blocks for the four HOPE structural components . Many of these elements contribute to multiple HOPE components ; thus , instead of being organized by structural component , they are broadly described here as belief systems , social interaction norms , and rituals . Importantly , their existence does not imply that they have been adopted appropriately and by large number of Hualish members .
The HOPE programming model is presented , using both an informal description of its primitives , and a definition of its formal semantics . We then describe the abstract algorithms for implementing HOPE in a distributed environment , as well as the prototype HOPE system that embeds the HOPE primitives in C using the PVM message passing library . Finally , we introduce the performance characteristics of the prototype HOPE system , and describe future research directions .
2 The Approach and the HOPE Framework The approach . We propose the High-level Objective-based Policy for Enterprises ( HOPE ) framework which is aimed at addressing the above research problems via policy-based design and management . As presented in Figure 1 , with HOPE , we specify quality requirements ( which are driven by original business rules and regulations ) in the form of business-level policies which will then be formulated as quality objectives applicable on business entities .
Also in SPM , it recommends items , regardless of whether user has purchased that item before or not . This thesis proposes an E-commerce recommendation system , SEERs ( Stacking Ensemble E-commerce Recommendation system ) , which improves on HOPE system to make better recommendations in the following two ways : i ) Learning the best minimum support for SPA , best k similar users for CF and the best weights for integrating the four used matrices . ii ) Separating their two intermediate matrices of CFPP and SPAPP into four intermediate matrices of CF_notpurchased , SPM_purchased , SPM_notpurchased and purchasehistory matrix which are obtained and merged with the better-learned parameters from ( i ) above .
In order to select the appropriate protocol , each protocolpsilas performance should be compared across realistic missions with different traffic profiles , terrain , and atmospheric conditions . The HOPE tool is a framework that combines terrain maps , simulation models of communication protocols , and link budget calculations . This framework allows communication protocol models from various simulations to be visualized on one user interface .
Since these nodes have varying capabilities , services running atop are also faced with profound performance unpredictability . In this paper , we propose a Heterogeneous netwOrk Policy Enforcement ( HOPE ) scheme to overcome these challenges . HOPE guarantees that network functions ( NFs ) that implement a policy chain are optimally placed onto heterogeneous NFBs such that the network cost of the policy is minimized .
Second , given a certain order proximity , the existing methods can not guarantee accuracy and efficiency simultaneously . To address these challenges , we propose AROPE ( arbitrary-order proximity preserved embedding ) , a novel network embedding method based on SVD framework . We theoretically prove the eigen-decomposition reweighting theorem , revealing the intrinsic relationship between proximities of different orders .
The usefulness of NEMII is further validated by case studies . The studies demonstrate the great potential of network embedding method for the miRNA-disease association prediction , and SDNE outperforms other popular network embedding methods : DeepWalk , High-Order Proximity preserved Embedding ( HOPE ) and Laplacian Eigenmaps (LE).ConclusionWe propose a new method , named NEMII , for predicting miRNA-disease associations , which has great potential to benefit the field of miRNA-disease association prediction .
Higher-order proximity preserved network embedding has attracted increasing attention recently . In particular , due to the superior scalability , random-walk based network embedding has also been well developed , which could efficiently explore higher-order neighborhood via multi-hop random walks .
In these applications , the goal is to find a sparsest Steiner k-TC-spanner of a poset G for a given k and G. The focus of this paper is the relationship between the dimension of a poset and the size of its sparsest Steiner TC-spanner . The dimension of a poset G is the smallest d such that G can be embedded into a d-dimensional directed hypergrid via an order-preserving embedding . We present a nearly tight lower bound on the size of Steiner 2-TC-spanners of d- dimensional directed hypergrids .
Specifically , we find that MNMF , a community preserving embedding method , is the most competitive method for the link prediction task . While NetMF is the most competitive baseline for node classification . Third , no single method completely outperforms other embedding methods on both node classification and link prediction tasks .
We further provide the theoretical connections between skip-gram based network embedding algorithms and the theory of graph Laplacian . Finally , we present the NetMF method as well as its approximation algorithm for computing network embedding . Our method offers significant improvements over DeepWalk and LINE for conventional network mining tasks .
Class representative computation using graph embedding and clustering Graph embedding-based approach for detecting group shilling attacks in collaborative recommender systems Incremental Graph Embedding Based on Spatial-Spectral Neighbors for Hyperspectral Image Classification Spatial context driven manifold learning for hyperspectral image classification LINE: Large-scale Information Network Embedding Embedding of Embedding (EOE): Joint Embedding for Coupled Heterogeneous Networks Approximate Deep Network Embedding for Mining Large-Scale Graphs Integrating multi-network topology for gene function prediction using deep neural networks Enhancing Network Embedding with Auxiliary Information: An Explicit Matrix Factorization Perspective Database Systems for Advanced Applications: 23rd International Conference, DASFAA 2018, Gold Coast, QLD, Australia, May 21-24, 2018, Proceedings, Part I The Application of Network Based Embedding in Local Topic Detection from Social Media Efficient Fault-Tolerant Topology Reconfiguration Using a Maximum Flow Algorithm An Event-contrastive Connectome Network for Automatic Assessment of Individual Face Processing and Memory Ability LINE: Large-scale Information Network Embedding Discovering Urban Functional Areas Based on Node2vec - Taking Shanghai as an Example Improved DeepWalk Algorithm Based on Preference Random Walk Learning Word Embeddings from Portuguese Lexical-Semantic Knowledge Bases N2VSCDNNR: A Local Recommender System Based on Node2vec and Rich Information Network FCA2VEC: Embedding Techniques for Formal Concept Analysis Prediction of Missing Semantic Relations in Lexical-Semantic Network using Random Forest Classifier Network Embedding as Matrix Factorization: Unifying DeepWalk, LINE, PTE, and node2vec Seq2seq Dependency Parsing Characterizing and Detecting Money Laundering Activities on the Bitcoin Network Reinforcement Learning for Optimal Control of Network Epidemic Processes dynnode2vec: Scalable Dynamic Network Embedding Semi-Supervised Classification with Graph Convolutional Networks node2vec: Scalable Feature Learning for Networks From Rateless to Hopless The Heritage of the People's Europe Project: An Aggregative Data Infrastructure for Cultural Heritage Hybrid Orthogonal Projection and Estimation (HOPE): A New Framework to Probe and Learn Neural Networks Current Hualish Structure A programming model for optimism Quality-driven Design and Management of Service-oriented Software Systems E-commerce Recommendation by an Ensemble of Purchase Matrices with Sequential Patterns The network protocol evaluation with the Hybrid Operational Protocol Evaluation tool Enabling Heterogeneous Network Function Chaining Arbitrary-Order Proximity Preserved Network Embedding A network embedding-based multiple information integration method for the MiRNA-disease association prediction RWNE: A Scalable Random-Walk based Network Embedding Framework with Personalized Higher-order Proximity Preserved Steiner transitive-closure spanners of low-dimensional posets Network Representation Learning: Consolidation and Renewed Bearing Network Embedding as Matrix Factorization: Unifying DeepWalk, LINE, PTE, and node2vec
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SIU WHISPERS WWW '15 WSDM CogMI DASFAA (1) ICTAI ICASSP WWW '15 NLPCC (1) PROPOR WSDM COLING BigData ICLR KDD IRCDL ICSOC PhD Symposium KDD WSDM
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