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Method of periodic dynamic pattern mining based on complex network

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 efficient approach to mine maximal continuous frequent patterns from software function call sequence

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Journal of Computational Information systems 2015年第8期11卷 3073-3085页

作者： He, Haitao Yin, Tengteng Wu, Hongfei Bai, Lan 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 Institute of Foreign Languages Qinhuangdao China

Certain characteristics of software are often hidden in its structure and can only be discovered when the software is executed dynamically. So mining important patterns from dynamic call graph of software plays an important role in analyzing software security. However the general sequential pattern mining algorithms may lead to poor performance due to the lack of consideration on the continuous property of software function call sequence. In this paper, combining with the characteristics of software itself, function call sequence is modeled firstly. The sequence represents the call relationship among functions of software. Then a novel structure called Nitem-List is put forward. The structure stores both the next item and the support information which is heuristic information for pruning the search space about a pattern. On the basis of that, an efficient algorithm called Maximal Continuous Frequent Pattern Miner (MCFP-Miner) and an update algorithm UMCFP-Miner are proposed. After constructing the initial Nitem-Lists from a mined database, MCFP-Miner can mine continuous frequent patterns from these Nitem-Lists. In UMCFP-Miner, a tighter bound for pruning is proposed. Finally, extensive experiments on various databases show that algorithm MCFP-Miner and UMCFP-Miner have better efficiency. ©, 2015, Binary Information Press. All right reserved.

关键词： Miners

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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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Influence maximization in MicroBlog based on a new user influence ranking method

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Journal of Information and Computational Science 2015年第9期12卷 3729-3737页

作者： Liu, Yuanying Guo, Jingfeng Hu, Xinzhuan Liu, Miaomiao Zhang, Jilong College of Information Science and Engineering Yanshan University Qinhuangdao China College of Information Technology Hebei University of Economics and Business Shijiazhuang China The Key Laboratory for Computer Virtual Technology and System Integration of Hebei Province Qinhuangdao China

The characteristics of the MicroBlog data are analyzed firstly. Then WeiBo graph is constructed through tweets forwarded path and user influence ranking calculation method which according to the classic PageRank is given;the initial value of user influence ranking concludes three attributes, such as followers' number, tweets' number and recent tweets' quality. Maximization algorithm in MicroBlog based on user influence ranking is put forward finally. Experiments show that this maximization algorithm is superior to the others in spread scope. ©, 2015, Journal of Information and Computational Science. All right reserved.

关键词： Computational methods

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A Generalized Weighted Closed Sequential Pattern Mining Algorithm with Item Interval

A Generalized Weighted Closed Sequential Pattern Mining Algo...

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2015 International Conference on Automation,Mechanical Control and Computational Engineering(AMCCE 2015)

作者： Haitao Lu Shuo Li College of Information Science and Engineering Yanshan University The Key Laboratory for Computer Virtual Technology and System Integration of Hebei Province Library of Yanshan University Yanshan University

ISBN: (纸本)9781510803084

The algorithm of this paper inserts pseudo items which are converted from item interval to obtain equal extended sequence database;it defines item-interval constraints, which are relative to the item weight, to prune the mining patterns. Through doing this, the algorithm avoids mining the patterns which users are not interested in and shortens the running time. It adopts histogram statistic pattern to get the standardization description to item interval of the mining patterns, making the mining sequences include the item interval information which is valuable to user decision.

关键词： weighted sequential patterns closed sequential patterns item interval

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A community detection method to undirected weighted signed social networks

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Journal of Computational Information systems 2015年第10期11卷 3623-3632页

作者： Guo, Jingfeng Liu, Miaomiao Liu, Linlin Liu, Yuanying 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 Northeast Petroleum University Daqing China

With regard to the community mining in weighted signed networks and weighted social networks containing only positive links, the AGMA algorithm has some imperfections in the second clustering of vertices. So some improvements were made to it. And a new algorithm CD WSSN (Community Detection for Weighted Signed Social Networks) is proposed to detect communities in undirected and unweighted signed networks. It takes the sign of the edge, the weight of the edge and the friend coefficient between the vertex and community as clustering criteria of vertices. It can divide a weighted signed network into the right number of communities accurately and effectively. Experiments were done on datasets like weighted signed networks, weighted traditional social networks, unweighted signed networks as well as standard networks. The comparative analysis of the experimental results were also done with regard to the difference and frustration about CD WSSN, AGMA, CRA algorithm, and so on. The results verify the accuracy and generality of the algorithm. ©, 2015, Binary Information Press. All right reserved.

关键词： Population dynamics

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Mining the important nodes of software based on complex networks

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ICIC Express Letters 2015年第12期9卷 3263-3268页

作者： Huang, Guoyan Zhang, Peng Li, Yanling Ren, Jiadong College of Information Science and Engineering Yanshan University No. 438 Hebei Ave Qinhuangdao China The Key Laboratory for Computer Virtual Technology and System Integration of Hebei Province Yanshan University No. 438 Hebei Ave Qinhuangdao China

The main purpose of studying the software structure is considered to understand and recognize the structure easily by also taking into account the quality of software. In this paper, we define and analyze the node importance based on the invoking dependency relationships among the nodes to mine the influential nodes for the software. Firstly, we construct a directed network to denote the software execution process depending on the invoking relationships. Secondly, according to the information of the Dependency Node Set (DNS) of each function node, we define the importance for the node. Finally, we present the algorithm Measure the Importance of Node (MIN) to evaluate the importance of each node. Comparing the value of nodes by the algorithm Mining the top-K Nodes (MKN), we can obtain the most influential nodes in the software network. In addition, with experiments results, the method MKN is verified to be effective for measuring the importance of the function nodes accurately. © 2015 ICIC International.

关键词： Complex networks

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The constructions of ternary sequence pairs based on cyclotomy

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ICIC Express Letters 2015年第6期9卷 1761-1767页

作者： Jia, Yanguo Duan, Xiaobei Shi, Yan Shen, Xiumin School of Information Sciences and Engineering Yanshan University No. 438 Hebei Avenue Qinhuangdao China The Key Laboratory for Computer Virtual Technology and System Integration of Heibei Province No. 438 Hebei Avenue Qinhuangdao China

In this paper, two new methods of constructing ternary sequence pairs based on cyclotomy are presented. The ternary sequence pairs with three-level correlation are constructed based on cyclotomic numbers of order 4. And the ternary sequence pairs with four-level correlation are constructed based on cyclotomic numbers of order 6. The two methods cover all the prime period respectively. © 2015 ICIC International.

关键词： computers

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