Experiments conducted on real-world datasets demonstrate that our proposals achieve a competitive performance with methods from the literature and , in most of the cases , outperform them . Moreover , our proposals use less computational resources by reducing considerably the number of irrelevant predictions , making the link prediction task more efficient and applicable for real world applications . Link prediction is the problem of predicting the existence of edges in a network . The link prediction problem is a fundamental research problem in graph mining and has numerous applications in social networks , bioinformatics , e-commerce , etc . A major challenge of link prediction problems is handling the fact that real-world networks are becoming extremely large . Over the years , so many network embedding models have been worked upon based on several approaches . In this paper , we study vector embeddings of 10 different representation learning models , with the sole aim of carrying out two machine learning tasks on these learned representations – unsupervised community clustering and link prediction analysis . The goal is to compare the output of these tasks using the 10 models , and draw inference based on the obtained results . Then , a classifier is trained for each property . It finally conducts link prediction process using an ensemble result of all the classifiers . Experiments in three realistic collaboration networks show that the evolution information of the network is beneficial for the improvement of link prediction performance and different structural property has different capability to describe dynamics of the network . The features and classification of the ad-hoc routing protocols & explains the description of Ad-hoc on demand distance vector ( AODV ) protocol with its mechanisms . This paper also discusses the link failure in mobile ad-hoc networks and the link state prediction is illustrated . First established in the multicast tree , MAODV-SP protocol uses a neighbor change rate stability based on the path selection mechanism , each node on the path according to different rate of change of tired by the neighbor value , choose a small hop forward and a small change in the stability of local topological path data forwarding . Second , in the multicast tree maintenance , MAODV-SP uses a link state protocol link availability prediction mechanism for node mobility and link state prediction , the predicted link chain scission will occur prior to re-select a new and effective route to forward packets to the network to achieve optimal performance . These networks are regarded as Multiplex Networks . This work presents an approach for link prediction on Multiplex Networks where the associations are learned from the multiple layer of networks for link prediction purposes . These networks are regarded as Multiplex Networks . MethodsThis work presents an approach for link prediction in Multiplex networks where the associations are learned from the multiple layers of networks for link prediction purposes . Most of the real-world networks are represented as weighted networks . In academic social networks , prediction of relationships is typically used to try to identify potential partners in the development of a project and/or co-authors for publishing papers . This paper presents an approach to predict coauthorships combining artificial intelligence techniques with the state-of-the-art metrics for link predicting in social networks . Ten years of development shows that knowledge network can be used as an ideal carrier for link prediction , and link prediction is a powerful tool for analyzing knowledge network . On this basis , carrying out applied research from link prediction perspective based on the structure and evolution of knowledge network will be the future research direction of knowledge network in the field of Library and Information Science . 5 figs. 2 tabs . Other than the method of deriving these relation matrices , the key difference to existing factorization approaches is the ReLU non - linearity applied prior to interaction with the object embedding . section : Link Prediction In link prediction , the aim is to learn a scoring function that assigns a score to each input triple , where are subject and object entities and a relation . document : Learning to Make Predictions on Graphs with Autoencoders We examine two fundamental tasks associated with graph representation learning : link prediction and semi - supervised node classification . We present a novel autoencoder architecture capable of learning a joint representation of both local graph structure and available node features for the multi - task learning of link prediction and node classification . However , they typically contain only a small subset of all possible facts . Link prediction is a task of inferring missing facts based on existing ones . We propose TuckER , a relatively simple but powerful linear model based on Tucker decomposition of the binary tensor representation of knowledge graph triples . Finally , we show that several previous state - of - the - art linear models , RESCAL , DistMult , ComplEx and SimplE , are special cases of TuckER . In summary , the main contributions of this paper are : proposing TuckER , a new linear model for link prediction in knowledge graphs , that is simple , expressive and achieves state - of - the - art results across all standard datasets ; proving that TuckER is fully expressive and deriving a bound on the entity and relation embedding dimensionality for full expressiveness which is several orders of magnitude lower than the bound of previous state - of - the - art models ComplEx and SimplE ; and showing that TuckER subsumes several previously proposed tensor factorization approaches to link prediction , i.e. that RESCAL , DistMult , ComplEx and SimplE are all special cases of our model . section : Related Work We use the filtered setting only , i.e. we remove all other true triples apart from the currently observed test triple . For evaluation , we use two evaluation metrics used across the link prediction literature : mean reciprocal rank ( MRR ) and hits@ , . Mean reciprocal rank is the average of the inverse of a mean rank assigned to the true triple over all generated triples . section : Conclusion In this work , we introduce TuckER , a relatively simple yet highly flexible linear model for link prediction in knowledge graphs based on the Tucker decomposition of a third - order binary tensor of training set triples , which achieves state - of - the - art results on standard link prediction datasets . As well as being fully expressive , TuckER ’s number of parameters grows linearly with respect to embedding dimension as the number of entities or relations in a knowledge graph increases . In statistical relational learning , the link prediction problem is key to automatically understand the structure of large knowledge bases . As in previous studies , we propose to solve this problem through latent factorization . Compared to state - of - the - art models such as Neural Tensor Network and Holographic Embeddings , our approach based on complex embeddings is arguably simpler , as it only uses the Hermitian dot product , the complex counterpart of the standard dot product between real vectors . Our approach is scalable to large datasets as it remains linear in both space and time , while consistently outperforming alternative approaches on standard link prediction benchmarks . ruled algothplop algoAlgorithm ComplexEmbeddingsforSimpleLinkPrediction section : Conclusion We described a simple approach to matrix and tensor factorization for link prediction data that uses vectors with complex values and retains the mathematical definition of the dot product . The class of normal matrices is a natural fit for binary relations , and using the real part allows for efficient approximation of any learnable relation . Logical process of early warning system of water security based on GIS is expounded taking Liaohe River for an example . System functions that involve information collection , query and search , assessment and analysis , dynamic prediction and early warning , guide and management are designed primarily according to its aim and principle . The client-browser-server network structures are designed . We further generalize the results to the more flexible landmark linear transformation models that include the landmark Cox model as a special case . These results facilitate future theoretical and numerical research on landmark survival models for dynamic prediction . With the two-player game training and learning strategy , GDLP is capable of accurate prediction for dynamic networks using the structural and temporal information . Experimental results validate that GDLP significantly outperforms several existing baseline methods on many types of dynamic networks , which improves the effectiveness of dynamic link prediction . Dynamic link prediction is a research hot in complex networks area , especially for its wide applications in biology , social network , economy and industry . Compared with static link prediction , dynamic one is much more difficult since network structure evolves over time . Currently most researches focus on static link prediction which can not achieve expected performance in dynamic network . Aiming at low AUC , high Error Rate , add/remove link prediction difficulty , we propose GC-LSTM , a Graph Convolution Network ( GC ) embedded Long Short Term Memory network ( LTSM ) , for end-to-end dynamic link prediction . To the best of our knowledge , it is the first time that GCN embedded LSTM is put forward for link prediction of dynamic networks . GCN in this new deep model is capable of node structure learning of network snapshot for each time slide , while LSTM is responsible for temporal feature learning for network snapshot . However , none of the previous work has considered the dynamic nature of real-world systems . In this work , we present the first study of adversarial attack on dynamic network link prediction ( DNLP ) . The proposed attack method , namely time-aware gradient attack ( TGA ) , utilizes the gradient information generated by deep dynamic network embedding ( DDNE ) across different snapshots to rewire a few links , so as to make DDNE fail to predict target links . Using data from two multidisciplinary research domains other than the medical domain , capitalizing on bibliometrics , the dynamicity of temporal KCNs , and a Long Short Term Memory recurrent neural network , this study proposed a framework to successfully predict the future literature-based discoveries - the emerging connections among knowledge units . Framing the problem as a dynamic supervised link prediction task , the proposed framework integrates some novel node and edge-level features . Temporal importance of keywords computed from both bipartite and unipartite networks , communities of keywords , built upon genealogical relations , and relative importance of temporal citation counts used in the feature construction process . Using data from two multidisciplinary research domains other than the medical domain , capitalizing on bibliometrics , the dynamicity of temporal KCNs , and a Long Short Term Memory recurrent neural network , this study proposed a framework to successfully predict the future literature-based discoveries - the emerging connections among knowledge units . Framing the problem as a dynamic supervised link prediction task , the proposed framework integrates some novel node and edge-level features . Temporal importance of keywords computed from both bipartite and unipartite networks , communities of keywords , built upon genealogical relations , and relative importance of temporal citation counts used in the feature construction process . The paper mentions that the temporal regression model for the dynamic graph link prediction problem rests on spectral graph theory and low-rank approximation for the graph Laplacian matrix . However , most studies just focused on link prediction of static network , while real-world networks always evolve over time with the occurrence and vanishing of nodes and links . Dynamic network link prediction thus has been attracting more and more attention since it can better capture the evolution nature of networks , but still most algorithms fail to achieve satisfied prediction accuracy . Motivated by the excellent performance of Long Short-Term Memory ( LSTM ) in processing time series , in this paper , we propose a novel Encoder-LSTM-Decoder ( E-LSTM-D ) deep learning model to predict dynamic links end to end . The invention provides a prediction method of the motion path of a vehicle in a tunnel . The method comprises the steps that a number of cameras are arranged at user-specified interval in the tunnel ; a number of photos are shot in user-specified period of time ; a photo processing result and a photo library are matched to determine the sign information of the vehicle ; thresholding , median filtering and a series of processing are carried out on the photos ; a motion vector is combined to update a next position of the target vehicle in the tunnel ; and vehicle tracking and dynamic prediction of are completed . According to the invention , information in the photos can be effectively acquired ; the information is timely fed back to a driver ; and a rich content base is provided for accurately analyzing the running condition of the vehicle in the tunnel . Our framework marries ideas from point process models , including Poisson and Hawkes processes , with ideas from latent space models of static networks . We evaluate our models over a range of tasks on real-world datasets and show that a dual latent space model , which accounts for heterogeneity in both reciprocity and homophily , significantly improves performance for both static and dynamic link prediction . In this paper , we concentrated on predicting the formation of such anchor links between heterogeneous social networks . Unlike conventional link prediction problems in which the formation of a link between two existing users within a single network is predicted , in anchor link prediction , the target user is missing and will be added to the target network once the anchor link is created . To solve this problem , we propose an effective general meta-path-based approach called Connector and Recursive Meta-Paths ( CRMP ) . To combat the challenges , in this paper we propose a novel embedding and matching architecture to directly learn binary hash code for each node . Hash codes offer us an efficient index to filter out the candidate node pairs for anchor link prediction . Extensive experiments on synthetic and real world large-scale datasets demonstrate that our proposed method has high time efficiency without loss of competitive prediction accuracy in anchor link prediction . Formally , the correspondences of the accounts that belong to the same user are defined as anchor links , and the networks aligned by anchor links can be denoted as aligned networks . In this paper , we study the problem of anchor link prediction ( ALP ) across a pair of aligned networks based on social network structure . First , three similarity metrics ( CPS , CCS , and CPS+ ) are proposed . Formally , social networks that outline the relationships of a common group of people are defined as aligned networks , and the correspondence of the accounts that refer to the same natural person across aligned networks are defined as anchor links . In this paper , we learn the problem of Anchor Link Prediction ( ALP ) . Firstly , two similarity metrics ( Bi-Similarity BiS and Reliability Similarity ReS ) are proposed to measure the similarity between nodes in aligned networks . Predicting anchor links across social networks has important implications to an array of applications , including cross-network information diffusion and cross-domain recommendation . One challenging problem is : whether and to what extent we can address the anchor link prediction problem , if only structural information of networks is available . Most existing methods , unsupervised or supervised , directly work on networks themselves rather than on their intrinsic structural regularities , and thus their effectiveness is sensitive to the high dimension and sparsity of networks . Prevailing researches reveal that link prediction problem complexity , available solutions effective group communication management and social link consciousness . The link prediction problem across associated networks can include anchor link prediction problem and link transfer through associated heterogeneous networks . This paper summarizes recent growth about link prediction algorithms and survey of all the prevailing link prediction techniques . Critically , these anchor points enable nonparametric regression that predicts the label for each data point as a locally weighted average of the labels on anchor points . Because conventional graph construction is inefficient in large scale , we propose to construct a tractable large graph by coupling anchor-based label prediction and adjacency matrix design . Contrary to the Nystrom approximation of adjacency matrices which results in indefinite graph Laplacians and in turn leads to potential non-convex optimization over graphs , the proposed graph construction approach based on a unique idea called AnchorGraph provides nonnegative adjacency matrices to guarantee positive semidefinite graph Laplacians . Due to the expensive costs of labeling anchor users for training prediction models , we consider in this paper the problem of minimizing the number of user pairs across multiple networks for labeling as to improve the accuracy of the prediction . To this end , we present a deep active learning model for anchor user prediction ( DALAUP for short ) . However , active learning for anchor user sampling meets the challenges of non-i.i.d . Visual aesthetics surveys among architects and challenges in porous material selection for sound absorbing products are also discussed . The implemented methodology , calibration of prediction models with measurements and design decisions including the “ backup strategy ” successfully demonstrated the achievement of the product aims in respect of both sound absorption and aesthetics , as well as other boundaries of the process . ( 5 ) The auxiliary judgment for the sampling points : the method is helpful to users in establishing the best sampling points . ( 6 ) The calibration of prediction results : we use calibration to correct the prediction error arisen from Li . We also derive the predictive distribution of the discrepancy term between the real process and the computer model that can be used to validate the computer model . Furthermore , in order to efficiently use limited data resources to obtain a better calibration and prediction performance , we propose a two-stage sequential approach which can effectively allocate the limited resources . The accuracy and efficiency of the proposed approach are illustrated by the numerical examples . The framework consists of three main components : ( 1 ) an approximate method for generating initial probability estimates of the edge types for each pair of variables , ( 2 ) the availability of a relatively small number of the causal relationships in the network for which the truth status is known , which we call a calibration training set , and ( 3 ) a calibration method for using the approximate probability estimates and the calibration training set to generate calibrated probabilities for the many remaining pairs of variables . Our experiments on a range of simulated data support that the proposed approach improves the calibration of edge predictions . The results also support that the approach often improves the precision and recall of those predictions . Intended applications include real-time network traffic management , vehicle routing and information provision . The framework integrates methods for receiving a stream of probe vehicle data , map matching and path inference , link travel time estimation , calibration of prediction model parameters and network travel time prediction in real time . The system design satisfies three crucial aspects : computational efficiency of prediction , internal consistency between components and robustness against noisy and missing data .
Experiments conducted on real-world datasets demonstrate that our proposals achieve a competitive performance with methods from the literature and , in most of the cases , outperform them . Moreover , our proposals use less computational resources by reducing considerably the number of irrelevant predictions , making the link prediction task more efficient and applicable for real world applications .
Link prediction is the problem of predicting the existence of edges in a network . The link prediction problem is a fundamental research problem in graph mining and has numerous applications in social networks , bioinformatics , e-commerce , etc . A major challenge of link prediction problems is handling the fact that real-world networks are becoming extremely large .
Over the years , so many network embedding models have been worked upon based on several approaches . In this paper , we study vector embeddings of 10 different representation learning models , with the sole aim of carrying out two machine learning tasks on these learned representations – unsupervised community clustering and link prediction analysis . The goal is to compare the output of these tasks using the 10 models , and draw inference based on the obtained results .
Then , a classifier is trained for each property . It finally conducts link prediction process using an ensemble result of all the classifiers . Experiments in three realistic collaboration networks show that the evolution information of the network is beneficial for the improvement of link prediction performance and different structural property has different capability to describe dynamics of the network .
The features and classification of the ad-hoc routing protocols & explains the description of Ad-hoc on demand distance vector ( AODV ) protocol with its mechanisms . This paper also discusses the link failure in mobile ad-hoc networks and the link state prediction is illustrated .
First established in the multicast tree , MAODV-SP protocol uses a neighbor change rate stability based on the path selection mechanism , each node on the path according to different rate of change of tired by the neighbor value , choose a small hop forward and a small change in the stability of local topological path data forwarding . Second , in the multicast tree maintenance , MAODV-SP uses a link state protocol link availability prediction mechanism for node mobility and link state prediction , the predicted link chain scission will occur prior to re-select a new and effective route to forward packets to the network to achieve optimal performance .
These networks are regarded as Multiplex Networks . This work presents an approach for link prediction on Multiplex Networks where the associations are learned from the multiple layer of networks for link prediction purposes .
These networks are regarded as Multiplex Networks . MethodsThis work presents an approach for link prediction in Multiplex networks where the associations are learned from the multiple layers of networks for link prediction purposes . Most of the real-world networks are represented as weighted networks .
In academic social networks , prediction of relationships is typically used to try to identify potential partners in the development of a project and/or co-authors for publishing papers . This paper presents an approach to predict coauthorships combining artificial intelligence techniques with the state-of-the-art metrics for link predicting in social networks .
Ten years of development shows that knowledge network can be used as an ideal carrier for link prediction , and link prediction is a powerful tool for analyzing knowledge network . On this basis , carrying out applied research from link prediction perspective based on the structure and evolution of knowledge network will be the future research direction of knowledge network in the field of Library and Information Science . 5 figs. 2 tabs .
Other than the method of deriving these relation matrices , the key difference to existing factorization approaches is the ReLU non - linearity applied prior to interaction with the object embedding . section : Link Prediction In link prediction , the aim is to learn a scoring function that assigns a score to each input triple , where are subject and object entities and a relation .
document : Learning to Make Predictions on Graphs with Autoencoders We examine two fundamental tasks associated with graph representation learning : link prediction and semi - supervised node classification . We present a novel autoencoder architecture capable of learning a joint representation of both local graph structure and available node features for the multi - task learning of link prediction and node classification .
However , they typically contain only a small subset of all possible facts . Link prediction is a task of inferring missing facts based on existing ones . We propose TuckER , a relatively simple but powerful linear model based on Tucker decomposition of the binary tensor representation of knowledge graph triples .
Finally , we show that several previous state - of - the - art linear models , RESCAL , DistMult , ComplEx and SimplE , are special cases of TuckER . In summary , the main contributions of this paper are : proposing TuckER , a new linear model for link prediction in knowledge graphs , that is simple , expressive and achieves state - of - the - art results across all standard datasets ; proving that TuckER is fully expressive and deriving a bound on the entity and relation embedding dimensionality for full expressiveness which is several orders of magnitude lower than the bound of previous state - of - the - art models ComplEx and SimplE ; and showing that TuckER subsumes several previously proposed tensor factorization approaches to link prediction , i.e. that RESCAL , DistMult , ComplEx and SimplE are all special cases of our model . section : Related Work
We use the filtered setting only , i.e. we remove all other true triples apart from the currently observed test triple . For evaluation , we use two evaluation metrics used across the link prediction literature : mean reciprocal rank ( MRR ) and hits@ , . Mean reciprocal rank is the average of the inverse of a mean rank assigned to the true triple over all generated triples .
section : Conclusion In this work , we introduce TuckER , a relatively simple yet highly flexible linear model for link prediction in knowledge graphs based on the Tucker decomposition of a third - order binary tensor of training set triples , which achieves state - of - the - art results on standard link prediction datasets . As well as being fully expressive , TuckER ’s number of parameters grows linearly with respect to embedding dimension as the number of entities or relations in a knowledge graph increases .
In statistical relational learning , the link prediction problem is key to automatically understand the structure of large knowledge bases . As in previous studies , we propose to solve this problem through latent factorization .
Compared to state - of - the - art models such as Neural Tensor Network and Holographic Embeddings , our approach based on complex embeddings is arguably simpler , as it only uses the Hermitian dot product , the complex counterpart of the standard dot product between real vectors . Our approach is scalable to large datasets as it remains linear in both space and time , while consistently outperforming alternative approaches on standard link prediction benchmarks . ruled algothplop algoAlgorithm ComplexEmbeddingsforSimpleLinkPrediction
section : Conclusion We described a simple approach to matrix and tensor factorization for link prediction data that uses vectors with complex values and retains the mathematical definition of the dot product . The class of normal matrices is a natural fit for binary relations , and using the real part allows for efficient approximation of any learnable relation .
Logical process of early warning system of water security based on GIS is expounded taking Liaohe River for an example . System functions that involve information collection , query and search , assessment and analysis , dynamic prediction and early warning , guide and management are designed primarily according to its aim and principle . The client-browser-server network structures are designed .
We further generalize the results to the more flexible landmark linear transformation models that include the landmark Cox model as a special case . These results facilitate future theoretical and numerical research on landmark survival models for dynamic prediction .
With the two-player game training and learning strategy , GDLP is capable of accurate prediction for dynamic networks using the structural and temporal information . Experimental results validate that GDLP significantly outperforms several existing baseline methods on many types of dynamic networks , which improves the effectiveness of dynamic link prediction .
Dynamic link prediction is a research hot in complex networks area , especially for its wide applications in biology , social network , economy and industry . Compared with static link prediction , dynamic one is much more difficult since network structure evolves over time . Currently most researches focus on static link prediction which can not achieve expected performance in dynamic network . Aiming at low AUC , high Error Rate , add/remove link prediction difficulty , we propose GC-LSTM , a Graph Convolution Network ( GC ) embedded Long Short Term Memory network ( LTSM ) , for end-to-end dynamic link prediction . To the best of our knowledge , it is the first time that GCN embedded LSTM is put forward for link prediction of dynamic networks . GCN in this new deep model is capable of node structure learning of network snapshot for each time slide , while LSTM is responsible for temporal feature learning for network snapshot .
However , none of the previous work has considered the dynamic nature of real-world systems . In this work , we present the first study of adversarial attack on dynamic network link prediction ( DNLP ) . The proposed attack method , namely time-aware gradient attack ( TGA ) , utilizes the gradient information generated by deep dynamic network embedding ( DDNE ) across different snapshots to rewire a few links , so as to make DDNE fail to predict target links .
Using data from two multidisciplinary research domains other than the medical domain , capitalizing on bibliometrics , the dynamicity of temporal KCNs , and a Long Short Term Memory recurrent neural network , this study proposed a framework to successfully predict the future literature-based discoveries - the emerging connections among knowledge units . Framing the problem as a dynamic supervised link prediction task , the proposed framework integrates some novel node and edge-level features . Temporal importance of keywords computed from both bipartite and unipartite networks , communities of keywords , built upon genealogical relations , and relative importance of temporal citation counts used in the feature construction process .
Using data from two multidisciplinary research domains other than the medical domain , capitalizing on bibliometrics , the dynamicity of temporal KCNs , and a Long Short Term Memory recurrent neural network , this study proposed a framework to successfully predict the future literature-based discoveries - the emerging connections among knowledge units . Framing the problem as a dynamic supervised link prediction task , the proposed framework integrates some novel node and edge-level features . Temporal importance of keywords computed from both bipartite and unipartite networks , communities of keywords , built upon genealogical relations , and relative importance of temporal citation counts used in the feature construction process .
The paper mentions that the temporal regression model for the dynamic graph link prediction problem rests on spectral graph theory and low-rank approximation for the graph Laplacian matrix .
However , most studies just focused on link prediction of static network , while real-world networks always evolve over time with the occurrence and vanishing of nodes and links . Dynamic network link prediction thus has been attracting more and more attention since it can better capture the evolution nature of networks , but still most algorithms fail to achieve satisfied prediction accuracy . Motivated by the excellent performance of Long Short-Term Memory ( LSTM ) in processing time series , in this paper , we propose a novel Encoder-LSTM-Decoder ( E-LSTM-D ) deep learning model to predict dynamic links end to end .
The invention provides a prediction method of the motion path of a vehicle in a tunnel . The method comprises the steps that a number of cameras are arranged at user-specified interval in the tunnel ; a number of photos are shot in user-specified period of time ; a photo processing result and a photo library are matched to determine the sign information of the vehicle ; thresholding , median filtering and a series of processing are carried out on the photos ; a motion vector is combined to update a next position of the target vehicle in the tunnel ; and vehicle tracking and dynamic prediction of are completed . According to the invention , information in the photos can be effectively acquired ; the information is timely fed back to a driver ; and a rich content base is provided for accurately analyzing the running condition of the vehicle in the tunnel .
Our framework marries ideas from point process models , including Poisson and Hawkes processes , with ideas from latent space models of static networks . We evaluate our models over a range of tasks on real-world datasets and show that a dual latent space model , which accounts for heterogeneity in both reciprocity and homophily , significantly improves performance for both static and dynamic link prediction .
In this paper , we concentrated on predicting the formation of such anchor links between heterogeneous social networks . Unlike conventional link prediction problems in which the formation of a link between two existing users within a single network is predicted , in anchor link prediction , the target user is missing and will be added to the target network once the anchor link is created . To solve this problem , we propose an effective general meta-path-based approach called Connector and Recursive Meta-Paths ( CRMP ) .
To combat the challenges , in this paper we propose a novel embedding and matching architecture to directly learn binary hash code for each node . Hash codes offer us an efficient index to filter out the candidate node pairs for anchor link prediction . Extensive experiments on synthetic and real world large-scale datasets demonstrate that our proposed method has high time efficiency without loss of competitive prediction accuracy in anchor link prediction .
Formally , the correspondences of the accounts that belong to the same user are defined as anchor links , and the networks aligned by anchor links can be denoted as aligned networks . In this paper , we study the problem of anchor link prediction ( ALP ) across a pair of aligned networks based on social network structure . First , three similarity metrics ( CPS , CCS , and CPS+ ) are proposed .
Formally , social networks that outline the relationships of a common group of people are defined as aligned networks , and the correspondence of the accounts that refer to the same natural person across aligned networks are defined as anchor links . In this paper , we learn the problem of Anchor Link Prediction ( ALP ) . Firstly , two similarity metrics ( Bi-Similarity BiS and Reliability Similarity ReS ) are proposed to measure the similarity between nodes in aligned networks .
Predicting anchor links across social networks has important implications to an array of applications , including cross-network information diffusion and cross-domain recommendation . One challenging problem is : whether and to what extent we can address the anchor link prediction problem , if only structural information of networks is available . Most existing methods , unsupervised or supervised , directly work on networks themselves rather than on their intrinsic structural regularities , and thus their effectiveness is sensitive to the high dimension and sparsity of networks .
Prevailing researches reveal that link prediction problem complexity , available solutions effective group communication management and social link consciousness . The link prediction problem across associated networks can include anchor link prediction problem and link transfer through associated heterogeneous networks . This paper summarizes recent growth about link prediction algorithms and survey of all the prevailing link prediction techniques .
Critically , these anchor points enable nonparametric regression that predicts the label for each data point as a locally weighted average of the labels on anchor points . Because conventional graph construction is inefficient in large scale , we propose to construct a tractable large graph by coupling anchor-based label prediction and adjacency matrix design . Contrary to the Nystrom approximation of adjacency matrices which results in indefinite graph Laplacians and in turn leads to potential non-convex optimization over graphs , the proposed graph construction approach based on a unique idea called AnchorGraph provides nonnegative adjacency matrices to guarantee positive semidefinite graph Laplacians .
Due to the expensive costs of labeling anchor users for training prediction models , we consider in this paper the problem of minimizing the number of user pairs across multiple networks for labeling as to improve the accuracy of the prediction . To this end , we present a deep active learning model for anchor user prediction ( DALAUP for short ) . However , active learning for anchor user sampling meets the challenges of non-i.i.d .
Visual aesthetics surveys among architects and challenges in porous material selection for sound absorbing products are also discussed . The implemented methodology , calibration of prediction models with measurements and design decisions including the “ backup strategy ” successfully demonstrated the achievement of the product aims in respect of both sound absorption and aesthetics , as well as other boundaries of the process .
( 5 ) The auxiliary judgment for the sampling points : the method is helpful to users in establishing the best sampling points . ( 6 ) The calibration of prediction results : we use calibration to correct the prediction error arisen from Li .
We also derive the predictive distribution of the discrepancy term between the real process and the computer model that can be used to validate the computer model . Furthermore , in order to efficiently use limited data resources to obtain a better calibration and prediction performance , we propose a two-stage sequential approach which can effectively allocate the limited resources . The accuracy and efficiency of the proposed approach are illustrated by the numerical examples .
The framework consists of three main components : ( 1 ) an approximate method for generating initial probability estimates of the edge types for each pair of variables , ( 2 ) the availability of a relatively small number of the causal relationships in the network for which the truth status is known , which we call a calibration training set , and ( 3 ) a calibration method for using the approximate probability estimates and the calibration training set to generate calibrated probabilities for the many remaining pairs of variables . Our experiments on a range of simulated data support that the proposed approach improves the calibration of edge predictions . The results also support that the approach often improves the precision and recall of those predictions .
Intended applications include real-time network traffic management , vehicle routing and information provision . The framework integrates methods for receiving a stream of probe vehicle data , map matching and path inference , link travel time estimation , calibration of prediction model parameters and network travel time prediction in real time . The system design satisfies three crucial aspects : computational efficiency of prediction , internal consistency between components and robustness against noisy and missing data .
Exploiting social and mobility patterns for friendship prediction in location-based social networks Link Prediction via Factorization Machines Evaluating Network Embedding Models for Machine Learning Tasks A supervised link prediction method for dynamic networks A Survey: Ad-hoc on Demand Distance Vector (AODV) Protocol Ad Hoc Network Routing Protocol ZRP Research and Simulation An Efficient Method for Link Prediction in Complex Multiplex Networks An efficient method for link prediction in weighted multiplex networks Co-authorship prediction in academic social network A Review on Link Prediction of Scientific Know ledge Netw ork Hypernetwork Knowledge Graph Embeddings Learning to Make Predictions on Graphs with Autoencoders TuckER: Tensor Factorization for Knowledge Graph Completion Complex Embeddings for Simple Link Prediction Design of an early warning system of water security for Liaohe River based on GIS On the Landmark Survival Model for Dynamic Prediction of Event Occurrence Using Longitudinal Data Generative dynamic link prediction. GC-LSTM: Graph Convolution Embedded LSTM for Dynamic Link Prediction Time-aware Gradient Attack on Dynamic Network Link Prediction Mining Temporal Evolution of Knowledge Graph and Genealogical Features for Literature-based Discovery Prediction Towards an LSTM-based Predictive Framework for Literature-based Knowledge Discovery Spectral Regression with Low-Rank Approximation for Dynamic Graph Link Prediction E-LSTM-D: A Deep Learning Framework for Dynamic Network Link Prediction Prediction method of motion path of vehicle in tunnel Decoupling Homophily and Reciprocity with Latent Space Network Models Predicting anchor links between heterogeneous social networks Learning Binary Hash Codes for Fast Anchor Link Retrieval across Networks Inferring Anchor Links Based on Social Network Structure Anchor Link Prediction Using Topological Information in Social Networks Predict Anchor Links across Social Networks via an Embedding Approach A Review on Link Prediction in Social Network Large Graph Construction for Scalable Semi-Supervised Learning Deep Active Learning for Anchor User Prediction Design, simulations and experimental research in the process of development of sound absorbing perforated ceiling tile Planning system for indoor wireless network A sequential approach for stochastic computer model calibration and prediction Obtaining Accurate Probabilities Using Classifier Calibration. Integrated framework for real-time urban network travel time prediction on sparse probe data
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ICPR Australasian Conference on Artificial Intelligence COMPLEX NETWORKS (1) SITIS ICANN 2018 IEEE 5th International Conference on Data Science and Advanced Analytics (DSAA) EMNLP/IJCNLP ICML UAI ASONAM WWW WAIM (1) IJCAI ICML IJCAI
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