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New friend recommendation with user interest and socialization

Journal of Information and Computational Science 2015年第11期12卷 4253-4262页

作者： Huang, Guoyan Chang, Yixin Li, Yakun College of Information Science and Engineering Yanshan University Qinhuangdao China The Key Laboratory for Computer Virtual Technology and System Integration of Hebei Province Qinhuangdao China

Friend recommendation is popular in social site to help people make new friends and expand their social networks. However, the conventional friend recommendation method is low accuracy for the sparsity of data and cold start. To solve this problem, we propose a new personalized recommendation method with user interest and socialization. This approach incorporating user interest and social information mainly leverages both user-based collaborative filtering method and user topological structure in social networks. Firstly, user interest degree can be obtained to identify the user preferences. Secondly, get the new optimized similarity degree and the target user's nearest neighbor by using social collaborative filtering algorithm. In the end, the final recommendation set of user can be acquired from the highest predicted rating of the candidate collection. The experiments indicate that this proposed method can enhance the Effectiveness and accuracy of recommendation compared with the traditional friend recommendation algorithm. ©, 2015, Binary Information Press. All right reserved.

关键词： Collaborative filtering

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Grid resource dynamic replication strategy based on access frequency and time cost

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Journal of Computational Information systems 2015年第6期11卷 1973-1982页

作者： Zhang, Zhongping He, Long The School of Information Science and Engineering Yanshan University Qinhuangdao China The Key Laboratory for Computer Virtual Technology and System Integration of Hebei Province Qinhuangdao China

Data grid provides a large amount of computing resources and storage space to handle data-intensive applications. Because of the limitation of available resources and the production of large-scale workload in grid, the effective use of grid resources has become a major challenge. Data replication can reduce the bandwidth consumption, improve the fault-tolerant ability and enhance the extensibility. A grid resources dynamic replication strategy based on the access frequency and time cost is given in this paper. The proposed strategy deletes the file by considering two important parameters: access frequency and transmission costs. In the replica selection strategy, the best replica is selected by considering the waiting time of the replica request queue in the storage and the data transfer time. The simulated results with OptorSim show that the proposed replication strategy gives better performance compared with DHRA algorithm. Copyright © 2015 Binary Information Press.

关键词： Data transfer

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A novel approach to efficiently mine structural patterns from software execution sequence

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Journal of Computational Information systems 2015年第3期11卷 1109-1119页

作者： Huang, Guoyan Zhang, Bing Ren, Rong Ren, Jiadong College of Information Science and Engineering Yanshan University Qinhuangdao China The Key Laboratory for Computer Virtual Technology and System Integration of Hebei Province Qinhuangdao China

Analyzing structure of a software system is not sufficient for understanding its functionality. Certain characteristics of software are often hidden in its structure and can only be discovered when the various execution sequence of the software are identified. Analysis of execution sequence, therefore, becomes an indispensable aid in software comprehension. In this paper, a novel approach to efficiently mine structural patterns from software execution sequence is proposed. First, software execution traces are modeled as sequential patterns. Due to loops, patterns can occur multiple times within a trace, which leads to high cost of time and super complexity of the research. Then, an algorithm is designed to eliminate repetitive patterns (ERP) in software and software sequential pattern mining (SSPM) algorithm is presented to divide software unique sequential patterns into different patterns in order to mine structural characteristics of the software. At last, by the comparative analysis of similarity between different pattern collections, it draws the conclusion that software system can be compartmentalized into n fixed patterns. Experimental results also prove the conclusion well. ©, 2015, Binary Information Press. All right reserved.

关键词： computer software

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Non-human-like characters' facial animation driven by one or two actors

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Journal of Information and Computational Science 2015年第10期12卷 3937-3944页

作者： Tang, Yong Liu, Yuhan Guo, Dan Liang, Haiyan Luo, Yanqing Lv, Mengya College of Information Science and Engineering Yanshan University Qinhuangdao China Key Laboratory for Computer Virtual Technology and System Integration of Hebei Province Qinhuangdao China

We present a novel system that can transfer facial expressions from an actor to a non-human-like character. Compared with a lot of previous studies, our system requires no special light conditions or facial markers for actors' performance. More significantly, our work can find vertex correspondences between the human face and characters with dissimilar facial structures by creating local-coordinate systems on the character. Experiments show that in our system not only one actor's performance is accurately transferred to non-human-like characters but also two actors' expressions can be simultaneously retargeted to one non-human-like character. Through our approach, virtual characters can have much more vivid expressions. 1548-7741/Copyright © 2015 Binary Information Press

关键词： Animation

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Mining maximal frequent patterns based on hierarchical bit vector and stack principle

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Journal of Computational Information systems 2015年第1期11卷 369-376页

作者： Wang, Qian Ren, Jiadong He, Haitao Huang, Guoyan College of Information Science and Engineering Yanshan University Qinhuangdao China The Key Laboratory for Computer Virtual Technology and System Integration of Hebei Province Qinhuangdao China

Maximal frequent pattern mining usually adopts a global pattern growth way, and a maximal frequent pattern can be obtained unless most of its subsets are checked to be frequent. An algorithm for mining maximal frequent patterns based on hierarchical bit vector and stack principle, called MaxPat HB, is proposed in this paper. Different with the traditional thinking of "test after connection", MaxPat HB adopts an individual pattern growth way and uses another thought of "testing while growing" which can efficiently gain the specified maximal frequent pattern and decrease the scale of candidate generation. The original data is converted to hierarchical bit vectors and maximal frequent patterns are mined hierarchically according to item unit. Candidate itemset generation adopts the stack principle, the push and pop operation is imitated by transforming bit values in a vector. Logic calculations between bit vectors are faster than the matches between strings, and the hierarchical structure can extremely reduce the searching space. Experiment result shows its high efficiency and good scalability. ©, 2014, Journal of Computational Information systems. All right reserved.

关键词： Vectors

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Research on Router Algorithm Based on Trust Primary Node in the Environment of P2P Network

Research on Router Algorithm Based on Trust Primary Node in ...

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2015 International Conference on Informatics, Control and Automation(ICA 2015)

作者： Jing CHEN Yun WAN Department of Computer College of Information Science and EngineeringYanshan University Key Laboratory of Computer Virtual Technology and System Integration of Hebei Province

The distributed, heterogeneous and anonymous attribute of P2 P networks make the node information sharing facing security problems. In order to prevent malicious and false service of nodes, and improve the retrieval efficiency, the primary node selection algorithm based on trust mechanism is proposed in this paper. Firstly, the network topology based on virtual community is described, and the function of primary node is discussed. Secondly, the primary node is selected according to direct trust value and recommendation trust value of each node in the virtual community. Finally, resource retrieval algorithm is designed according to the selected trust primary node, and the algorithm is experimentally verified. In some certain extent, the results show that the method is able to reduce the fraud of primary node and improve the retrieval efficiency.

关键词： P2P network virtual community Primary node Trust mechanism Router algorithm

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Method of periodic dynamic pattern mining based on complex network

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Journal of Computational Information systems 2015年第21期11卷 7849-7856页

作者： Wang, Lei Jiang, Liya Dong, Jun Huang, Guoyan Ren, Jiadong College of Information Science and Engineering Yanshan University Qinhuangdao China The Key Laboratory for Computer Virtual Technology and System Integration of Hebei Province Qinhuangdao China Hebei Normal University of Science and Technology Qinhuangdao China

Traditionally, analysis of dynamic network has been focused only on a single snapshot or integrated network obtained over a period of time. However, the temporal feature in dynamic network has been ignored. In this paper, periodic dynamic pattern (PDP) mining method is proposed to discover the dynamic evolution rule of social networks. We define a periodic dynamic pattern based on complex network, and construct matrix of graph (MG) to present the evolution of complex network over time. Moreover, a regular edge pattern searching (REPS) algorithm based on MG is designed. It is used to select frequent and regular edge existence sequence (EES) in MG with progressive scan. To improve efficiency of the algorithm, sequences according with frequency threshold and REP should be transformed to decimal integer. Then, depth-first search method is used to mine PDP, which owns the same REP in dynamic network. So, we can predict future network evolution and control its behavior. Experimental results demonstrate that our method is an effective periodic dynamic pattern mining. Copyright © 2015 Binary Information Press.

关键词： Complex networks

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Frequent jump pattern mining based on complex network

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Journal of Computational Information systems 2015年第17期11卷 6451-6458页

Many researchers are devoted to find frequent pattern in static network. Due to these frequent patterns are usually defined as frequent existence patterns which satisfy given support threshold in network. However, the temporal feature in dynamic network is ignored. In this paper, frequent jump pattern (FJP) is proposed, it not only represents frequent pattern but also can show regular behavior in evolving social network. Furthermore, jump patterns are robust against noise in the analysis of dynamical process. We construct matrix of directed graph (MDG) to present the evolution of complex directed network over time. Moreover, a frequent jump pattern searching algorithm (FJPSA) based on MDG is proposed to find dynamic evolution rule of social network. FJP mining is a technique which can be used in many application fields, such as explaining biological evolution and mining customer behavior regularity in social network. Experimental results demonstrate that our method could effectively mining FJP-graph. © 2015, by Binary Information Press

关键词： Directed graphs

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A novel approach for mining important nodes in directed-weighted complex software network

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Journal of Computational Information systems 2015年第8期11卷 3059-3071页

作者： Ren, Jiadong Wu, Hongfei Yin, Tengteng Bai, Lan Zhang, Bing College of Information Science and Engineering Yanshan University Qinhuangdao China The Key Laboratory for Computer Virtual Technology and System Integration of Hebei Province Qinhuangdao China Hebei Institute of Foreign Languages Qinhuangdao China

If the software system is abstracted as a software network, its quality largely depends on the topology structure. A tiny fraction key function nodes play a critical role in improving the stability, reliability and robustness of complex software network. How to identify the top-k key nodes accurately and effectively becomes more and more significant both in theoretical and practical. In this paper, a novel approach based on semi-global information of mining top-k key nodes in directed-weighted complex software network is proposed. Firstly, we map the function and relationship of function calling or dependency to a directed function dependency network (DFDN). Secondly, an algorithm DFS-WEIGHT based on Depth-First-Search strategy is used to transform a DFDN into a directed-weighted function dependency network (DWFDN). Thirdly, we treat function nodes of DWFDN as two different roles - the caller or callee. Callers with high ability of accumulating vulnerabilities and callees with high ability of propagating vulnerabilities are considered as key nodes. Then, two metrics SABAC and SABPR are defined to measure the importance of callers and callees respectively. Finally, a Software Network key Node miNing (SN-KNN) algorithm is proposed to mine most important callers and callees. Experimental results indicate that the algorithm SN-KNN is effective in mining key nodes. ©, 2015, Binary Information Press. All right reserved.

关键词： Software reliability

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An improved algorithm for community discovery in weighted social networks

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Journal of Information and Computational Science 2015年第18期12卷 6873-6881页

作者： Guo, Jingfeng Liu, Miaomiao Liu, Linlin College of Information Science and Engineering Yanshan University QinhuangdaoHebei China Northeast Petroleum University DaqingHeilongjiang China The Key Laboratory for Computer Virtual Technology and System Integration of Hebei Province QinhuangdaoHebei China

The problems of AGMA (Automatic Graph Mining Algorithm) are improved and a novel algorithm, namely CRMA (Clustering Re-clustering Merging Algorithm) is proposed which can realize more reasonable community division for weighted social networks. Firstly, when re-clustering nodes, AGMA neglected the clustering situation of its neighbors and didn't take into account the weight of the edge connecting the current node and its unclustered neighbor, which led to unreasonable clustering. Aiming to this, the concept of the connection compactness is introduced. Then according to the node weight, the edge weight and the friend coefficient, each node would be clustered into the community has the largest connection compactness with itself. Secondly, for community division in some unweighted networks and signed networks, if there are many nodes whose clustered neighbors is less than its unclustered neighbors, the division results of AGMA tend to produce many small communities, which would result in the lower modularity. In view of this problem, the concepts of positive edge weight density, the cluster density and the connection coefficient between clusters are proposed. Lastly, we merge communities based on the above three concepts and the modularity is effectively enhanced finally. The higher correctness and better generality of the improved algorithm are verified through experiments. © 2015 by Binary Information Press.

关键词： Clustering algorithms

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