Finding crucial vertices is a key problem for improving the reliability and ensuring the effective operation of networks,solved by approaches based on multiple attribute decision that suffer from ignoring the correlat...Finding crucial vertices is a key problem for improving the reliability and ensuring the effective operation of networks,solved by approaches based on multiple attribute decision that suffer from ignoring the correlation among each attribute or the heterogeneity between attribute and structure. To overcome these problems, a novel vertex centrality approach, called VCJG, is proposed based on joint nonnegative matrix factorization and graph embedding. The potential attributes with linearly independent and the structure information are captured automatically in light of nonnegative matrix factorization for factorizing the weighted adjacent matrix and the structure matrix, which is generated by graph embedding. And the smoothness strategy is applied to eliminate the heterogeneity between attributes and structure by joint nonnegative matrix factorization. Then VCJG integrates the above steps to formulate an overall objective function, and obtain the ultimately potential attributes fused the structure information of network through optimizing the objective function. Finally, the attributes are combined with neighborhood rules to evaluate vertex's importance. Through comparative analyses with experiments on nine real-world networks, we demonstrate that the proposed approach outperforms nine state-of-the-art algorithms for identification of vital vertices with respect to correlation, monotonicity and accuracy of top-10 vertices ranking.展开更多
It is important to reduce data redundancy of stereo video in practical applications.In this paper,first,a data embedding method for stereo video(DEMSV)is investigated by embedding the encoding data into the reference ...It is important to reduce data redundancy of stereo video in practical applications.In this paper,first,a data embedding method for stereo video(DEMSV)is investigated by embedding the encoding data into the reference frame to encode stereo video.It can use only one channel to transfer all the video data and the receiver can choose a monocular video decoder or stereo video decoder adaptively.Then,introducing the joint prediction scheme in the coding process of DEMSV,we propose a novel data embedding method for H.264 stereo video codec with joint prediction scheme(DEMSV-JPS)to achieve high coding efficiency.Experimental results show that the proposed method can obtain high peak signal-to-noise ratio(PSNR)and compression ratio(at least 33 dB for the test sequence).Comparing the testing methods using JPS and without using JPS,we prove that JPS can further improve the objective and visual quality.DEMSV-JPS shows such advantages and will be suitable to applications in real-time environments of stereo-video transmission.展开更多
To solve the problem of missing many valid triples in knowledge graphs(KGs),a novel model based on a convolutional neural network(CNN)called ConvKG is proposed,which employs a joint learning strategy for knowledge gra...To solve the problem of missing many valid triples in knowledge graphs(KGs),a novel model based on a convolutional neural network(CNN)called ConvKG is proposed,which employs a joint learning strategy for knowledge graph completion(KGC).Related research work has shown the superiority of convolutional neural networks(CNNs)in extracting semantic features of triple embeddings.However,these researches use only one single-shaped filter and fail to extract semantic features of different granularity.To solve this problem,ConvKG exploits multi-shaped filters to co-convolute on the triple embeddings,joint learning semantic features of different granularity.Different shaped filters cover different sizes on the triple embeddings and capture pairwise interactions of different granularity among triple elements.Experimental results confirm the strength of joint learning,and compared with state-of-the-art CNN-based KGC models,ConvKG achieves the better mean rank(MR)and Hits@10 metrics on dataset WN18 RR,and the better MR on dataset FB15k-237.展开更多
基金Project supported by the National Natural Science Foundation of China (Grant Nos.62162040 and 11861045)。
文摘Finding crucial vertices is a key problem for improving the reliability and ensuring the effective operation of networks,solved by approaches based on multiple attribute decision that suffer from ignoring the correlation among each attribute or the heterogeneity between attribute and structure. To overcome these problems, a novel vertex centrality approach, called VCJG, is proposed based on joint nonnegative matrix factorization and graph embedding. The potential attributes with linearly independent and the structure information are captured automatically in light of nonnegative matrix factorization for factorizing the weighted adjacent matrix and the structure matrix, which is generated by graph embedding. And the smoothness strategy is applied to eliminate the heterogeneity between attributes and structure by joint nonnegative matrix factorization. Then VCJG integrates the above steps to formulate an overall objective function, and obtain the ultimately potential attributes fused the structure information of network through optimizing the objective function. Finally, the attributes are combined with neighborhood rules to evaluate vertex's importance. Through comparative analyses with experiments on nine real-world networks, we demonstrate that the proposed approach outperforms nine state-of-the-art algorithms for identification of vital vertices with respect to correlation, monotonicity and accuracy of top-10 vertices ranking.
基金Supported by the National Natural Science foundation of China(60832003)
文摘It is important to reduce data redundancy of stereo video in practical applications.In this paper,first,a data embedding method for stereo video(DEMSV)is investigated by embedding the encoding data into the reference frame to encode stereo video.It can use only one channel to transfer all the video data and the receiver can choose a monocular video decoder or stereo video decoder adaptively.Then,introducing the joint prediction scheme in the coding process of DEMSV,we propose a novel data embedding method for H.264 stereo video codec with joint prediction scheme(DEMSV-JPS)to achieve high coding efficiency.Experimental results show that the proposed method can obtain high peak signal-to-noise ratio(PSNR)and compression ratio(at least 33 dB for the test sequence).Comparing the testing methods using JPS and without using JPS,we prove that JPS can further improve the objective and visual quality.DEMSV-JPS shows such advantages and will be suitable to applications in real-time environments of stereo-video transmission.
基金Supported by the National Natural Science Foundation of China(No.61876144)。
文摘To solve the problem of missing many valid triples in knowledge graphs(KGs),a novel model based on a convolutional neural network(CNN)called ConvKG is proposed,which employs a joint learning strategy for knowledge graph completion(KGC).Related research work has shown the superiority of convolutional neural networks(CNNs)in extracting semantic features of triple embeddings.However,these researches use only one single-shaped filter and fail to extract semantic features of different granularity.To solve this problem,ConvKG exploits multi-shaped filters to co-convolute on the triple embeddings,joint learning semantic features of different granularity.Different shaped filters cover different sizes on the triple embeddings and capture pairwise interactions of different granularity among triple elements.Experimental results confirm the strength of joint learning,and compared with state-of-the-art CNN-based KGC models,ConvKG achieves the better mean rank(MR)and Hits@10 metrics on dataset WN18 RR,and the better MR on dataset FB15k-237.
