259 research outputs found
Improving information centrality of a node in complex networks by adding edges
The problem of increasing the centrality of a network node arises in many
practical applications. In this paper, we study the optimization problem of
maximizing the information centrality of a given node in a network
with nodes and edges, by creating new edges incident to . Since
is the reciprocal of the sum of resistance distance
between and all nodes, we alternatively consider the problem of minimizing
by adding new edges linked to . We show that the
objective function is monotone and supermodular. We provide a simple greedy
algorithm with an approximation factor and
running time. To speed up the computation, we also present an
algorithm to compute -approximate
resistance distance after iteratively adding edges, the
running time of which is for any
, where the notation suppresses the factors. We experimentally demonstrate the effectiveness and
efficiency of our proposed algorithms.Comment: 7 pages, 2 figures, ijcai-201
An investigation on Senior Students’ Behavioral Intention to Use Tencent Meeting for Legal Course in Chengdu, China
Purpose: This research aims to investigate senior students’ behavioral intention to use Tencent meeting for the legal course in Chengdu, China. The key variables are developed from previous literature, including perceived usefulness, attitude, social influence, perceived behavioral control, subjective norm, behavioral intention, and use behavior. Research design, data, and methodology: The target population is 500 fourth-year students at three selected universities who have experience using the Tencent platform for the law course. Probability and nonprobability are used, including judgmental, stratified random, and convenience sampling. Before the data collection, the Item Objective Congruence (IOC) Index and the pilot test (n=30) by Cronbach’s Alpha were assessed to ensure content validity and reliability. Confirmatory Factor Analysis (CFA) and Structural Equation Modeling (SEM) were used as statistical tools to confirm validity, reliability, and hypotheses testing. Results: The results show that all hypotheses are supported. Perceived usefulness significantly impacts attitude. Attitude, social influence, perceived behavioral control, and subjective norm significantly impacts behavioral intention. Furthermore, behavioral intention significantly impacts use behavior. Conclusions: Tencent meeting developers, college administrators, or practitioners should focus on improving students’ Tencent meeting use behavior. The developer of Tencent Meeting and the college’s top management should concentrate on making students’ perceptions of the app’s usefulness, social influence, and attitude
Assessment of Behavioral Intention to Use Tencent Meeting of First-Year Students for Legal Courses in Chengdu, China
Purpose: This research aims to assess the behavioral intention to use Tencent meetings of students for legal courses in Chengdu, China. The conceptual framework is developed from previous studies, incorporating perceived usefulness, attitude, social influence, perceived behavioral control, subjective norm, behavioral intention, and use behavior. Research design, data, and methodology: The target population is 500 first-year students at three selected universities who have experience using the Tencent platform for legal programs. The sample methods are judgmental, stratified random, and convenience sampling. Before the data collection, the Item Objective Congruence (IOC) Index and the pilot test (n=30) by Cronbach’s Alpha were assessed to ensure content validity and reliability. Confirmatory Factor Analysis (CFA) and Structural Equation Modeling (SEM) were used as statistical tools to confirm validity, reliability, and hypotheses testing. Results: The results show that all hypotheses are supported. Attitude, social influence, perceived behavioral control, and subjective norm significantly impacts behavioral intention and use behavior indirectly. Furthermore, perceived usefulness has a significant impact on attitude. Conclusions: The above key variables should be emphasized and strengthened to improve college students’ use behavior of Tencent meetings in the learning process. Universities ought to pay attention to enhancing a system to maximize students’ learning efficiency
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