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Predicting user influence based on improved linear threshold model in social networks

Journal of computational Information Systems 2014年第14期10卷 6151-6160页

作者： Tian, Zhongsheng Wang, Ying Zhao, Qiuyue Zuo, Wanli College of Software Jilin University Changchun China College of Computer Science and Technology Jilin University Changchun China Key Lab. of Symbolic Computation and Knowledge Engineering Attached to the Ministry of Education Changchun China

With the scale of social networks growing rapidly, the amount of user participating in it increases at astonishing speed. Predicting user influence in social networks is an interesting and useful research direction. There are lots of works on this area, mainly covering the following aspects: Maximize Influence, Influence Diffusion, Predicting Influential Users and Predicting social influence. The common issue of above aspects is that they can't calculate the precise user influence. In this work, we propose a novel method to give each user influence a precise value in social networks. The proposed method is based on Improved Linear Threshold Model (ILTM). To build ILTM, we take a series of measures to overcome the deficiencies brought by Linear Threshold Model, such as assigning random threshold to node, cold start. So we adopt user similarity instead of random threshold and utilize cluster algorithm and link analysis algorithm to surmount cold start. © 2014 by Binary Information Press

关键词： Forecasting

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Implicit knowledge discovery in biomedical ontologies: Computing interesting relatednesses

Implicit knowledge discovery in biomedical ontologies: Compu...

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IEEE International Conference on Bioinformatics and Biomedicine (BIBM)

作者： Tian Bai Leiguang Gong Casimir A. Kulikowski Lan Huang Key Laboratory of Symbolic Computation and Knowledge Engineering of Ministry of Education Jilin University Changchun China College of Computer Science and Technology Jilin University Changchun China Yantai Intelligent Information Technologies Ltd. Yantai China Department of Computer Science Rutgers The State University of New Jersey Piscataway NJ USA

Ontologies, seen as effective representations for sharing and reusing knowledge, have become increasingly important in biomedicine, usually focusing on taxonomic knowledge specific to a subject. Efforts have been made to uncover implicit knowledge within large biomedical ontologies by exploring semantic similarity and relatedness between concepts. However, much less attention has been paid to another potentially helpful approach: discovering implicit knowledge across multiple ontologies of different types, such as disease ontologies, symptom ontologies, and gene ontologies. In this paper, we propose a unified approach to the problem of ontology based implicit knowledge discovery - a Multi-Ontology Relatedness Model (MORM), which includes the formation of multiple related ontologies, a relatedness network and a formal inference mechanism based on set-theoretic operations. Experiments for biomedical applications have been carried out, and preliminary results show the potential value of the proposed approach for biomedical knowledge discovery.

关键词： Biological system modeling Biomedical measurement Ontologies Arthritis Diseases

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Identifying influential nodes in complex networks for network immunization

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Journal of computational Information Systems 2014年第20期10卷 8767-8774页

作者： Zhong, Lu Gao, Chao Zhang, Zili Shi, Ning Huang, Jiajin College of Computer and Information Science Southwest University Chongqing China Key Laboratory of Symbolic Computation and Knowledge Engineering of Ministry of Education Changchun China School of Information Technology Deakin University VIC Australia International WIC Institute Beijing University of Technology Beijing China

Identifying influential nodes is of theoretical significance in network immunization which is one of important methods to prevent virus propagation through protecting the influential nodes in a network. Lots of methods have been proposed to find these influential nodes based on the topological characteristics of a network (e.g., degree, betweenness or K-shell). Whereas due to the diversity of network topologies, these methods are not always effective in identifying influential nodes in any benchmark networks. We combine the advantages of existing methods based on attribute ranking and propose a universal ranking method, namely MAF (Multiple Attribute Fusion), to identify influential nodes from a complex network. We compare the efficiency of our proposed method with existing immunization strategies in different types of networks. Simulation results in the interactive email model show that the immunized nodes selected by MAF can restrain virus propagation effectively.

关键词： Complex networks

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Efficiently and fast learning a fine-grained stochastic blockmodel from large networks

Efficiently and fast learning a fine-grained stochastic bloc...

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18th Pacific-Asia Conference on Advances in knowledge Discovery and Data Mining, PAKDD 2014

作者： Zhao, Xuehua Yang, Bo Chen, Hechang College of Computer Science and Technology Jilin University Changchun China Key Laboratory of Symbolic Computation and Knowledge Engineer Ministry of Education Jilin University Changchun China

ISBN: (纸本)9783319066073

Stochastic blockmodel (SBM) has recently come into the spotlight in the domains of social network analysis and statistical machine learning, as it enables us to decompose and then analyze an exploratory network without knowing any priori information about its intrinsic structure. However, the prohibitive computational cost limits SBM learning algorithm with the capability of model selection to small network with hundreds of nodes. This paper presents a fine-gained SBM and its fast learning algorithm, named FSL, which ingeniously combines the component-wise EM (CEM) algorithm and minimum message length (MML) together to achieve the parallel learning of parameter estimation and model evaluation. The FSL significantly reduces the time complexity of the learning algorithm, and scales to network with thousands of nodes. The experimental results indicate that the FSL can achieve the best tradeoff between effectiveness and efficiency through greatly reducing learning time while preserving competitive learning accuracy. Moreover, it is noteworthy that our proposed method shows its excellent generalization ability through the application of link prediction. © 2014 Springer International Publishing.

关键词： Learning algorithms

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Video Sequence-Based Iris Recognition Inspired by Human Cognition Manner

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Journal of Bionic engineering 2014年第3期11卷 481-489页

作者： Yuanning Liu College of Computer Seience and Technology Jilin University Changchun 130012 P. R. China Key Laboratory of Symbolic Computation and Knowledge Engineering of Ministry of Education Jilin University Changchun 130012 P. R. China College of Software Nanchang HangKong University Nanchang 330063 P. R. China

In video sequence-based iris recognition system, the problem of making full use of relationship and correlation among frames still remains to be solved. A brand new template level multimodal fusion algorithm inspired by human cognition manner is proposed. In that a non-isolated geometrical manifold, named Hyper Sausage Chain due to its sausage shape, is trained using the frames from a pattern class for representing an iris class in feature space. We can classify any input iris by observing which manifold it locates in. This process is closer to the function of human being, which takes ＇matter cognition＇ instead of ＇matter classification＇ as its basic principle. The experiments on self-developed JLUBR-IRIS dataset with several video sequences per person demonstrate the effectiveness and usability of the proposed algorithm for video sequence-based iris recognition. Fur- thermore, the comparative experiments on public CASIA-I and CASIA-V4-Interval datasets show that our method can also achieve improved performance of image-based iris recognition system, provided enough samples are involved in training stage.

关键词： iris recognition video sequences multi-modal fusion bionic recognition hyper sausage neuron

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Integrity constraint validation for ontology using modularization

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Journal of Information and computational Science 2014年第8期11卷 2705-2713页

作者： Cui, Xianji Ouyang, Dantong Ye, Yuxin Su, Jing College of Computer Science and Technology Jilin University Changchun 130012 China Key Lab. of Symbolic Computation and Knowledge Engineering Ministry of Education Changchun 130012 China National Engineering Research Center of Geophysics Exploration Instruments Jilin University Changchun 130026 China

In the last years, integrity constraint validation over the ontology with expressive description logics is a prominent reasoning service in ontology engineering, as integrity constraints are added into the ontology to guarantee the legal states of ontology data. The integrity constraint validation can be converted into the conjunctive query answering, we thus check the satisfaction of integrity constraints in ontology knowledge base based on the tableau algorithm and further use the existing reasoning systems. With the increase of the ontology data set, however, these reasoning systems reveal the bad performance in real-life ontologies. In order to reduce the amount of information that must be taken into account, we apply the idea of modularization to break down the terminologies and assertions with respect to integrity constraints, and get smaller chunks for them. The empirical results are presented to demonstrate the high efficiency and an improvement of integrity constraint validation. Copyright © 2014 Binary Information Press.

关键词： Ontology

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Generalized Extended tanh-function Method for Traveling Wave Solutions of Nonlinear Physical Equations

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Communications in Mathematical Research 2014年第1期30卷 60-70页

作者： CHANG JING GAO YI-XIAN AND CAI HUA Fundamental Department Aviation University of Air Force College of Mathematics and Statistics Northeast Normal University Key Laboratory of Symbolic Computation and Knowledge Engineering of Ministry of Education Jilin University School of Mathematics Jilin University

In this paper, the generalized extended tanh-function method is used for constructing the traveling wave solutions of nonlinear evolution equations. We choose Fisher＇s equation, the nonlinear schr＆#168;odinger equation to illustrate the validity and ad-vantages of the method. Many new and more general traveling wave solutions are obtained. Furthermore, this method can also be applied to other nonlinear equations in physics.

关键词： generalized tanh-function method nonlinear Schrodinger equation,Fisher＇s equation

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computational prediction of human saliva-secreted proteins

Computational prediction of human saliva-secreted proteins

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10th International Symposium on Bioinformatics Research and Applications, ISBRA 2014

作者： Sun, Ying Zhou, Chunguang Wang, Jiaxin Cao, Zhongbo Du, Wei Wang, Yan Key Laboratory of Symbolic Computation and Knowledge Engineering of Ministry of Education College of Computer Science and Technology Jilin University Changchun 130012 China Nari Group Corporation State Grid Electric Power Research Institute Nanjing Jiangsu 211000 China

ISBN: (纸本)9783319081700

Using proteins in saliva as biomarkers has great advantage in early diagnosis and prognosis evaluation of health conditions or diseases. In this article, we present a computational method for predicting secreted proteins in human saliva. Firstly, we collected currently known saliva-secreted proteins and the representatives that deem to be not extracellular secretion into saliva. Secondly, we pruned the negative data concerned the imbalance condition, and then extracted the relevant features from the physicochemical and sequence properties of all remained proteins. After that, a support vector machine classifier was built which got performance of average sensitivity, specificity, precision, accuracy and Matthews correlation coefficient value to 80.67%, 90.56%, 90.09%, 85.53% and 0.7168, respectively. These results indicated that the selected features and the model are effective. Finally, a screening test was implemented to all human proteins in UniProt and acquired 5811 proteins as predicted saliva-secreted proteins which may be used as biomarker candidates for further salivary diagnosis. © 2014 Springer International Publishing Switzerland.

关键词： Biomarkers

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Dynamic biometric identification from multiple views using the GLBP-TOP method

Dynamic biometric identification from multiple views using t...

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作者： Wang, Yu Shen, Xuanjing Chen, Haipeng Zhai, Yujie College of Computer Science and Technology Jilin University Changchun130012 China Key Laboratory of Symbolic Computation Knowledge Engineering of Ministry of Education Jilin University Changchun Jilin130012 China Applied Technology College Jilin University Changchun130012 China

To realize effective and rapid dynamic biometric identification with low computational complexity, a video-based facial texture program that extracts local binary patterns from three orthogonal planes in the frequency domain of the Gabor transform (GLBP-TOP) was proposed. Firstly, each normalized face was transformed by Gabor wavelet to get the enhanced Gabor magnitude map, and then the LBP-TOP operator was applied to the maps to extract video texture. Finally, weighted Chi square statistics based on the Fisher Criterion were used to realize the identification. The proposed algorithm was proved effective through the biometric experiments using the Honda/UCSD database, and was robust against changes of illumination and expressions. © 2014 - IOS Press and the authors.

关键词： Local binary pattern

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Approaches to multiple attribute decision making with hesitant interval-valued fuzzy information under correlative environment

Approaches to multiple attribute decision making with hesita...

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作者： Zhu, Jian-Qi Fu, Feng Yin, Ke-Xin Luo, Jia-Qian Wei, Da College of Computer Science and Technology Jilin University Changchun China Key Laboratory of Symbolic Computation and Knowledge Engineering of Ministry of Education Jilin University Changchun China Xi'An Satellite Control Center Xi'an China College of Software Changchun University of Technology Changchun China

In this paper, we investigate the multiple attribute decision making (MADM) problems with hesitant interval-valued fuzzy information. Then, we further develop some new Einstein aggregation operators with hesitant interval-valued fuzzy information, such as the induced hesitant interval-valued fuzzy Einstein correlated averaging (I-HIVFECA) operator and induced hesitant interval-valued fuzzy Einstein correlated geometric (I-HIVFECG) operator. Then, we apply the induced hesitant interval-valued fuzzy Einstein correlated averaging (I-HIVFECA) operator and induced hesitant interval-valued fuzzy Einstein correlated geometric (I-HIVFECG) operator to deal with multiple attribute decision making under the hesitant interval-valued fuzzy environments. Finally, an illustrative example for evaluating software quality is given to verify the developed approach and to demonstrate its practicality and effectiveness. © 2014-IOS Press and the authors. All rights reserved.

关键词： Geometry

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