Slow task scheduling algorithm based on node identification by Yun-fei CUI, Xin-ming LI, Yi LI, Dong LIU

“…First of all, through the judgment of node ability and task execution time, slow node queue, very slow node queue and slow task queue were established. …”
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Reliable selfish node detection algorithm for opportunistic networks by Zhi REN, Yong-yin TAN, Ji-bi LI, Qian-bin CHEN

“…To address the problem of detection accuracy affected by situations like the omission of node receiving wrong frame and failure of monitoring beyond nodes'communication range during the consideration of the ex ing selfish node detection algorithms in opportunistic networks,a novel and reliable selfish node detection algorithm——RSND algorithm for opportunistic networks was proposed.It employs wrong frame analysis based on cross-layer monitoring mechanism,information excavation based on node encounter and node distance estimation based on RSSI three new mechanisms to eliminate the influence of node's selfishness detection due to wrong frame and failure of monitoring beyond nodes' communication range,improving the reliability of detection.Theoretical analysis verifies the effectiveness of RSND,and simulation results show that RSND can improve selfish node detection accuracy ratio and network throughput at least 6% and 4%,as compared to the existing selfish node detection algorithm based on 2-ACK and watchdog detection algorithm.…”
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The Local Triangle Structure Centrality Method to Rank Nodes in Networks by Xiaojian Ma, Yinghong Ma

“…The simulation results of the discriminability and the correctness compared with pairs of ranks (one is generated by SIR model and the others are generated by central nodes measures) show that LTSC outperforms some other local or semilocal methods in evaluating the node’s influence in most cases, such as degree, betweenness, H-index, local centrality, local structure centrality, K-shell, and S-shell. …”
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Node Classification on The Citation Network Using Graph Neural Network by Irani Hoeronis, Bambang Riyanto Trilaksono

“…Research on Graph Neural Networks has influenced various current real-world problems. The graph-based approach is considered capable of effectively representing the actual state of surrounding data by utilizing nodes, edges, and features. …”
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Analysis of factors influencing the electricity (capacity) price growth in the energy market of the Siberian Federal District by A. P. Dzyuba, D. V. Konopelko

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Link prediction method for dynamic networks based on matching degree of nodes by Cong LI, Xinsheng JI, Ushuxin LI, Jinsong LI, Haitao LI

“…The research of dynamic evolutionary trends and temporal characteristics in real networks which are important part of the real world is a hot research issue nowadays.As a typical research tool in the field of network science, link prediction technique can be used to predict the network evolution by mining the historical edge information and then predict the future edge.The topological evolution of dynamic real networks was analyzed and it found that the interaction and matching between nodes in the network topology can capture the dynamic characteristics of the network more comprehensively.The proposed method analyzed the attribute characteristics of network nodes, and defined a node importance quantification method based on primary and secondary influences.Besides, a time decay factor was introduced to portray the influence of network topology on the formation of connected edges at different moments.Furthermore, the node importance and time decay factor were combined to define the Temporal Matching Degree of Nodes (TMDN), which was used to measure the possibility of future edge formation between node pairs.The experimental results in five real dynamic network datasets showed that the proposed method achieves better prediction performance under both AUC and Ranking Score, with a maximum improvement of 42%.It also proved the existence of interactive matching priority among nodes, and confirmed the effectiveness of both primary and secondary influence of nodes.As the future work, we will add diversified feature information to further deepen the analysis of dynamic real networks and then predict the evolution law more accurately.…”
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Analysis of the Meshing Characteristic of Helical Gear based on Node Generation Technique by Feng Qiyin, Zhang Jun, Du Yuhong

“…Gear drive is used widely,and the probability of failure is very high.So it is very necessary to analyze the characteristic of the gear pair meshing.A method of using ANSYS APDL language node control technique to create the finite element model of helical gear is described,and an analysis example of using this modeling method is carried on.The distribution law of the stress and strain of the meshing process is clarified by analyzing.At the same time,the analysis of the influence of root fillet radius on the meshing character is carried out.The results show that the bending stress is decreased with the tooth root radius increases.…”
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Target Positioning with GDOP Assisted Nodes Selection Algorithm in Wireless Sensor Networks by Yunzhou Zhang, Dongfei Wei, Wenyan Fu, Bing Yang

“…In wireless sensor networks (WSN), the geometric distribution of anchor nodes has a significant influence on the positioning accuracy. …”
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TDoA localization-refused area analysis and node placement strategy research by Yue ZHAO, Zan LI, Bing LI, Benjian HAO

“…Regarding the Cramer-Rao lower bound (CRLB) as a localization accuracy metric, the sufficient conditions of the LRA in the time difference of arrival (TDoA)-based localization scenario were discussed and the influence of different factors on the LRA were analyzed.Furthermore, the node placement problem for wireless sensor network (WSN) was investigated by formulating an optimization problem with the objective as average CRLB, which was solved by a genetic algorithm based on LRA pre-judgment.Simulation validates the properties of the CRLB in a single-source scenario and verifies the sufficient condition and the spatial range of the LRA.The simulation also shows that the localization accuracy determined by the proposed algorithm is improved by 33.92% higher than the uniform angle array placement scheme, 13.74% compared to the regional vertex placement scheme, and 9.65% compared to the direct genetic algorithm.…”
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Restoration Strategy Based on Optimal Relay Node Placement in Wireless Sensor Networks by Xiaoding Wang, Li Xu, Shuming Zhou

“…However, the variability of distances between disjoint segments has tremendous influence on relay nodes deployment. In fact, finding the optimal solution for connecting disjoint segments in terms of the number and positions of relay nodes is NP-hard. …”
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Design of nodes importance assessment method for complex network based on neighborhood information by Xing LI, Jie ZHAN, Baoquan REN, Siqi ZHU

“…Accurate identification of influential nodes in complex networks is crucial for network management and network security.The local centrality method is concise and easy to use, but ignores the topological relationship between neighboring nodes and cannot provide globally optimal results.A node importance assessment method was proposed to correlate the node edge relationship and topology, which firstly applied the H-index and information entropy to assess the nodes, then combined the structural holes of the nodes with the structural characteristics of the nodes, and took into account the attribute of “bridging” while focusing on the node’s own quality and the amount of information about the neighboring nodes.The algorithm was validated by simulating the propagation process using the SIR model, and the Kendall correlation coefficient, complementary cumulative distribution function and propagation influence were applied to validate the validity and applicability of the method.Comparison of the experimental results on six real network datasets shows that the proposed method is more accurate than the traditional centrality methods in identifying and ordering the key nodes in the network.…”
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Microblog burst topic diffusion prediction algorithm based on the users and node scale by Wei WANG, Rui-guang LI, Yuan ZHOU, Wu YANG

“…Based on the scale of the user, topic diffusion prediction algorithms were proposed based on user and node scale respectively. Experiments show that the former can predict diffusion more accurately but with bad time complexity, and the latter node is more suitable for proc-essing large data sets.…”
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Differential Gene Expression in Primary Breast Tumors Associated with Lymph Node Metastasis by Rachel E. Ellsworth, Lori A. Field, Brad Love, Jennifer L. Kane, Jeffrey A. Hooke, Craig D. Shriver

“…Identification of molecular signatures discriminating lymph node-positive from lymph node-negative primary tumors would allow for stratification of patients requiring surgical assesment of lymph nodes. …”
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Supraclavicular lymph node metastasis in esophageal carcinoma: a topic of ongoing controversy by Bowen Zhang, Huan Zhang, Yu Chen, Wanli Xia, Yichun Wang

“…Lymph node metastasis is an important prognostic factor in esophageal carcinoma (EC). …”
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Male Gender Is Associated with Lymph Node Metastasis but Not with Recurrence in Papillary Thyroid Carcinoma by Jiang Zhu, Rui Huang, Ping Yu, Haoyu Ren, Xinliang Su

“…We aimed to clarify the influence of gender on the risk of developing lymph node metastasis (LNM) and on the prognosis of PTC patients. …”
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Mediastinal Lymph Node Metastases in Thyroid Cancer: Characteristics, Predictive Factors, and Prognosis by Ting-ting Zhang, Ning Qu, Jia-qian Hu, Rong-liang Shi, Duo Wen, Guo-hua Sun, Qing-hai Ji

“…Extrathyroidal extension and multiple mediastinal lymph nodes involved were the influence factors of prognosis in the thyroid cancer patients with MLNM.…”
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Research on dynamic evaluation method of individual scientific impact under multiple key nodes by Shuang Ma

“…The pr(y)-index of a 5-year time window is more sensitive to judge the of influence on scientific career. Conclusion. The two-node piecewise linear regression model successfully divided the academic trajectories of individual scientists into three stages。…”
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A New Metric for Modeling the Uneven Sleeping Problem in Coordinated Sensor Node Scheduling by Gaojuan Fan, Chongsheng Zhang

“…This new metric takes the influence of the boundary effects on node schedule into consideration. …”
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Improved Normalized Collinearity DV-Hop Algorithm for Node Localization in Wireless Sensor Network by Yunzhou Zhang, Shu Xiang, Wenyan Fu, Dongfei Wei

“…In recent years, collinearity theory is widely used in large-scale sensor network. When the anchor nodes are located at almost a straight line, the collinearity phenomenon will happen and usually cause negative influence on positioning accuracy. …”
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Finding influential nodes in complex networks based on Kullback–Leibler model within the neighborhood by Guan Wang, Zejun Sun, Tianqin Wang, Yuanzhe Li, Haifeng Hu

“…Finally, it assesses the damage influence of the removed node by integrating the network attributes and KL divergence model, thus achieving the evaluation of node importance. …”
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