Most existing network representation learning algorithms focus on network structures for learning.However,network structure is only one kind of view and feature for various networks,and it cannot fully reflect all cha...Most existing network representation learning algorithms focus on network structures for learning.However,network structure is only one kind of view and feature for various networks,and it cannot fully reflect all characteristics of networks.In fact,network vertices usually contain rich text information,which can be well utilized to learn text-enhanced network representations.Meanwhile,Matrix-Forest Index(MFI)has shown its high effectiveness and stability in link prediction tasks compared with other algorithms of link prediction.Both MFI and Inductive Matrix Completion(IMC)are not well applied with algorithmic frameworks of typical representation learning methods.Therefore,we proposed a novel semi-supervised algorithm,tri-party deep network representation learning using inductive matrix completion(TDNR).Based on inductive matrix completion algorithm,TDNR incorporates text features,the link certainty degrees of existing edges and the future link probabilities of non-existing edges into network representations.The experimental results demonstrated that TFNR outperforms other baselines on three real-world datasets.The visualizations of TDNR show that proposed algorithm is more discriminative than other unsupervised approaches.展开更多
Learner autonomy is a matter of explicit or conscious intention. Learners cannot accept responsibility for their own learning unless they have some ideas of what, why, and how they are trying to learn. This paper argu...Learner autonomy is a matter of explicit or conscious intention. Learners cannot accept responsibility for their own learning unless they have some ideas of what, why, and how they are trying to learn. This paper argues that learner autonomy may be promoted through strategy-based instruction. The intent of strategy-based instruction is to help all students become better language learners. When students begin to understand their own learning processes and can exert some control over these processes, they tend to take more responsibility for their own learning and become more autonomous. Teachers may conduct strategy-based instruction by complementing such activities as preparation, practice, evaluation and expansion.展开更多
基金Projects(11661069,61763041) supported by the National Natural Science Foundation of ChinaProject(IRT_15R40) supported by Changjiang Scholars and Innovative Research Team in University,ChinaProject(2017TS045) supported by the Fundamental Research Funds for the Central Universities,China
文摘Most existing network representation learning algorithms focus on network structures for learning.However,network structure is only one kind of view and feature for various networks,and it cannot fully reflect all characteristics of networks.In fact,network vertices usually contain rich text information,which can be well utilized to learn text-enhanced network representations.Meanwhile,Matrix-Forest Index(MFI)has shown its high effectiveness and stability in link prediction tasks compared with other algorithms of link prediction.Both MFI and Inductive Matrix Completion(IMC)are not well applied with algorithmic frameworks of typical representation learning methods.Therefore,we proposed a novel semi-supervised algorithm,tri-party deep network representation learning using inductive matrix completion(TDNR).Based on inductive matrix completion algorithm,TDNR incorporates text features,the link certainty degrees of existing edges and the future link probabilities of non-existing edges into network representations.The experimental results demonstrated that TFNR outperforms other baselines on three real-world datasets.The visualizations of TDNR show that proposed algorithm is more discriminative than other unsupervised approaches.
文摘Learner autonomy is a matter of explicit or conscious intention. Learners cannot accept responsibility for their own learning unless they have some ideas of what, why, and how they are trying to learn. This paper argues that learner autonomy may be promoted through strategy-based instruction. The intent of strategy-based instruction is to help all students become better language learners. When students begin to understand their own learning processes and can exert some control over these processes, they tend to take more responsibility for their own learning and become more autonomous. Teachers may conduct strategy-based instruction by complementing such activities as preparation, practice, evaluation and expansion.
