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14th International Conference on Mobile Ad-Hoc and Sensor Networks (MSN)

作者： Wang, Nan Li, Jinbao Da, Jiansong Liu, Yong Heilongjiang Univ Sch Comp Sci & Technol Harbin Heilongjiang Peoples R China Heilongjiang Univ Key Lab Database & Parallel Comp Harbin Heilongjiang Peoples R China

ISBN: (纸本)9781728105482

With the wide usage of many electronic devices such as the mobile telephones, the social activities in the mobile terminal have increased greatly. The influence maximization problem is to find a small set of nodes in a mobile social network such that they can make to maximize the spread of influence under the certain models. Existing methods of influence maximization only consider information dissemination, without considering the effectiveness of the transmission probabilities between nodes. We need to ensure the effectiveness of the transmission probabilities to achieve the maximal influence in real applications. We design a new propagation model and propose an effective greedy algorithm for this model. On this basis, we further improve the algorithmic efficiency with dynamic pruning strategy and propose a new estimation technique to accelerate influence evaluation. We demonstrate the effectiveness and efficiency of our algorithms on the two real social networks.

关键词： social relationship user similarity dynamic pruning strategy

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Friend and POI recommendation based on social trust cluster in location-based social networks

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EURASIP JOURNAL ON WIRELESS COMMUNICATIONS AND NETWORKING 2019年第1期2019卷 1页

作者： Zhu, Jinghua Wang, Chao Guo, Xu Ming, Qian Li, Jinbao Liu, Yong Heilongjiang Univ Sch Comp Sci & Technol XueFu Rd Harbin 150001 Heilongjiang Peoples R China Heilongjiang Prov Key Lab Database & Parallel Com XueFu Rd Harbin 150001 Heilongjiang Peoples R China

Friend and point-of-interest (POI) recommendation are two primary individual services in location-based social networks (LBSNs). Major social platforms such as Foursquare and Instagram are all capable of recommending friends or POIs to individuals. However, most of these social websites make recommendations only based on similarity, popularity, or geographical influence;social trust among individuals has not been considered in those recommendation system. Recently, trust relationship has been proved to be helpful in collaborative recommendation. In this paper, we first propose algorithm to identify trust clusters and then give a trust prediction method based on these trust clusters. Then we combine the trust value and similarity among individuals to recommend friends to the target user. As for the POI recommendation, we devise a hybrid framework that integrates user preference, geographical influence, and trust relationship to improve the recommendation quality. In order to validate the effectiveness and efficiency of our methods, a series of experiments on two real social networks Foursquare and Instagram are conducted. The experiment results show that the trust cluster-based recommendation approach outperforms the baseline recommendation approaches in precision and recall.

关键词： LBSN Friend recommendation POI recommendation Collaborative filtering Trust cluster

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Prediction of Customer Churn Incline in Mobile Communication 14

Prediction of Customer Churn Incline in Mobile Communication

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14th International Conference on Mobile Ad-Hoc and Sensor Networks (MSN)

作者： Tang, Jingfeng Li, Jinbao Wang, Nan Li, Biao Heilongjiang Univ Sch Comp Sci & Technol Harbin Heilongjiang Peoples R China Heilongjiang Univ Key Lab Database & Parallel Comp Heilongjiang Pro Harbin Heilongjiang Peoples R China

ISBN: (纸本)9781728105482

In order to solve the problems of retention time delay caused by the direct prediction for users' off-grid state and insufficient information mining caused by the too rough granularity of the monthly statistical features in the past, this paper presents the approach that applies serialized data analysis method to users' records serialized with Day as its granularity. Users' data would be sampled and analyzed by using the sliding windows. Then the churn incline among the users would be predicted in advance by collecting and sorting out their antecedent behavior occurring before their churn. The related experiments were conducted with real mobile communication records of social users and reached 98.22% recall and 0.98 F1(F1-measure) when it reached its highest level. Along with comparing with the traditional modeling which takes month as the granularity for its statistical item, the timeliness and predicting competency of the method presented in this article has been verified both theoretically and by the result of the test.

关键词： User Churn Incline Prediction Machine Learning Big-Data Analysis Mobile Communication Data Analysis State Transition Prediction User Retention

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Incremental Influence Maximization for Dynamic Social Networks 1

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3rd International Conference of Pioneering computer Scientists, Engineers and Educators (ICPCSEE)

作者： Wang, Yake Zhu, Jinghua Ming, Qian Heilongjiang Univ Sch Comp Sci & Technol Harbin 150001 Heilongjiang Peoples R China Key Lab Database & Parallel Comp Heilongjiang Pro Harbin Heilongjiang Peoples R China

ISBN: (数字)9789811063886

ISBN: (纸本)9789811063886;9789811063879

Influence maximization is one fundamental and important problem to identify a set of most influential individuals to develop effective viral marketing strategies in social network. Most existing studies mainly focus on designing efficient algorithms or heuristics to find Top-K influential individuals for static network. However, when the network is evolving over time, the static algorithms have to be re-executed which will incur tremendous execution time. In this paper, an incremental algorithm DIM is proposed which can efficiently identify the Top-K influential individuals in dynamic social network based on the previous information instead of calculating from scratch. DIM is designed for Linear Threshold Model and it consists of two phases: initial seeding and seeds updating. In order to further reduce the running time, two pruning strategies are designed for the seeds updating phase. We carried out extensive experiments on real dynamic social network and the experimental results demonstrate that our algorithms could achieve good performance in terms of influence spread and significantly outperform those traditional static algorithms with respect to running time.

关键词： Influence maximization Dynamic social network Linear threshold model Pruning strategy

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Research on Jukes-Cantor Model parallel Algorithm Based on OpenMP 1

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1st National Conference on Big Data Technology and Applications (BDTA)

作者： Wan, Kang Lu, Jun Zheng, Jiaju Ren, Zihan Heilongjiang Univ Coll Comp Sci & Technol Harbin Peoples R China Key Lab Database & Parallel Comp Heilongjiang Pro Harbin Peoples R China

ISBN: (数字)9789811004575

ISBN: (纸本)9789811004575;9789811004568

The parallel algorithm of the evolutionary distance between different species is implemented by using OpenMP parallel technique in this paper. In order to get the best degree of algorithm parallelism, the method of making loop variable corresponding to the rower and column labels of distance matrix is adopted. It is to say that the double loop can be converted into single loop to improve the parallel efficiency. The serial algorithm and parallel algorithm are compared in this paper. The experiment result shows that the highest speedup is 14.1. It improves the running efficiency of the program. It is a great significance to dealing with massive bioinformatics data.

关键词： OpenMP parallel Evolutionary distance Distance matrix

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A New Algorithm for Fast Mining Frequent Itemsets based on SO-Sets

A New Algorithm for Fast Mining Frequent Itemsets based on S...

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IEEE International Conference on Electronic Information and Communication Technology (ICEICT)

作者： Tani, Long Qin, Qibing Heilongjiang Univ Coll Comp Sci & Technol Harbin Peoples R China Key Lab Database & Parallel Comp Heilongjiang Pro Harbin Peoples R China

ISBN: (纸本)9781509007295

N-list and B-list have simply been proven to be highly effective for mining frequent itemsets. The main problem of the two novel structures is that they both need to encode each node of pre-order (or start order) and post-order (or finish order) code. This causes excessive memory consumption to mine frequent itemsets. In this paper, we propose SO-Sets based on SO-Tree, a more efficient data structure, to mine frequent itemsets. SO-Sets require only start-order (or finish-order) of each node, which makes it save lots of memory compared with Nlist and B-list. Based on SO-Sets, we propose a new algorithm called FISO to mining frequent itemsets. To analyze the performance of algorithms, we conduct lots of experiments on five real datasets. Experimental results show that FISO algorithm has advantages in running time and size of main memory consumption.

关键词： N-list B-list frequent itemsets SO-Tree SO-Sets

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Research on Linkage Disequilibrium Method based on OpenMP 3

Research on Linkage Disequilibrium Method based on OpenMP

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3rd International Conference on Information Science and Control Engineering (ICISCE)

作者： Lu, Jun Li, Jun Jing, Ruiqing Wang, Qiong Chang, Cheng Guo, Jiaxing Heilongjiang Univ Coll Comp Sci & Technol Harbin Peoples R China Key Lab Database & Parallel Comp Heilongjiang Pro Harbin Peoples R China

ISBN: (纸本)9781509025350

Linkage disequilibrium method is applied for the research on inferring population genetics, LD mapping, haploid type diversity analysis and so on. Soybean genotypes are adopted as the data source and linkage disequilibrium parallel algorithm is implemented by OpenMP technology. In this algorithm, single nucleotide polymorphism sites are divided by using sliding windows into groups, adjacent sites allele in a window of each chromosome are parallel calculated and store the LD results. According to the experimental data, the serial and parallel algorithms are compared and analyzed. The conclusion shows that the OpenMP parallel technology can effectively improve the efficiency of linkage disequilibrium analysis method. It is a realistic significance for processing massive biological information data.

关键词： OpenMP parallel LD SNPs

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An Efficient Mining Algorithm for Maximal Weighted Frequent Patterns Based on WIdT-Trees 17th

An Efficient Mining Algorithm for Maximal Weighted Frequent ...

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17th International Conference on Intelligent Data Engineering and Automated Learning (IDEAL)

作者： Qin, Qibing Tan, Long Heilongjiang Univ Coll Comp Sci & Technol Harbin Peoples R China Key Lab Database & Parallel Comp Heilongjiang Pro Harbin Peoples R China

ISBN: (纸本)9783319462578;9783319462561

As processed data is relatively dense or the support is small in weighted frequent patterns mining process, the number of frequent patterns which meet the conditions will be exponential growth, and mining all frequent patterns will need too much computation. Hence, mining the maximal weighted frequent patterns containing all frequent patterns has less calculation, and it has more utility value. Aiming at the process of maximal weighted frequent patterns mining, an efficient algorithm, based on WIdT-Trees, is proposed to discover maximal weighted frequent patterns. In the algorithm, WIdT-Tree is optimized from WIT-Tree. The dTidset strategy is used to calculate the weighted support of frequent k-itemsets, and the nodes with equal extended weighted support are pruned off in order to reduce the computational cost and decrease the search space complexity. Algorithms are tested and compared on real and synthetic datasets and experimental results show that our algorithm is more efficient and scalable.

关键词： Maximal weighted frequent patterns Widt-Tree dTidset Extended weighted support

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Research on Robust Relationship Inference in GWAS parallel Algorithm Based on OpenMP 3

Research on Robust Relationship Inference in GWAS Parallel A...

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3rd International Conference on Information Science and Control Engineering (ICISCE)

作者： Lu, Jun Jing, Ruiqing Li, Jun Liu, Xingkun Xu, Shenbao Zhang, Wei Heilongjiang Univ Coll Comp Sci & Technol Harbin Peoples R China Key Lab Database & Parallel Comp Heilongjiang Pro Harbin Peoples R China

ISBN: (纸本)9781509025350

In order to improve the efficiency of soybean genetic variation information, robust relationship inference in GWAS parallel algorithm is raised in this paper. This algorithm is adopted serial and parallel hybrid structure. Each row of data is parallel processed by OpenMP technique. The I/O processing and the accumulate processing are treated as a serial. Finally, the serial and parallel algorithms are compared and analyzed. The results reached theoretically exceptional speedup. The experimental results show that the execution efficiency of parallel algorithm has very big promotion than the serial algorithm. The soybean genetic variation information processing can be speed up effectively.

关键词： component parallel OpenMP robust relation value

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Data Dissemination Protocols Based on Opportunistic Sharing for Data Offloading in Mobile Social Networks 22

Data Dissemination Protocols Based on Opportunistic Sharing ...

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22nd IEEE International Conference on parallel and Distributed Systems (ICPADS)

作者： Jiang, Na Guo, Longjiang Li, Jinbao Ren, Meirui Cheng, Sisi Guo, Xiaodan Heilongjiang Univ Sch Comp Sci & Technol Harbin 150080 Peoples R China Key Lab Database & Parallel Comp Harbin 150080 Heilongjiang Peoples R China

ISBN: (纸本)9781509044573

Due to the increasing popularity of smart mobile devices, the amount of mobile data communications has led to explosive growth of data traffic in cellular networks. Cellular networks have to face the challenge of huge communication traffic. Offloading data traffic through opportunistic communication among smart mobile devices is a promising solution to partially solve this problem since there is almost no monetary cost for it. Large amount of smart mobile devices can communicate each other using Bluetooth or WIFI Direct in short communication range and they can form an opportunistic mobile social network. The opportunistic communications among smart mobile devices can effectively reduce the amount of cellular data traffic. However, mobile users take a long time to obtain useful data. In order to reduce data communication latency, this paper proposes three data dissemination protocols named RRDP(Request-Reply Dissemination Protocol), RDP(Random Dissemination Protocol) and LDP(LRU Dissemination Protocol) respectively. The three proposed protocols are based on opportunistic sharing policy. Extensive NS-2 simulation results show that (1) on the campus situation, the user's access delay of RDP is 56.4% less than the RRDP and LDP is 44.8% less than RRDP. (2) in the vehicular environment, the user's access delay of RDP is 32.5% less than the RRDP and LDP is 28.1% less than RRDP. RDP is the best protocol.

关键词： Cellular traffic offloading Opportunistic communications Request-Reply policy Random dissemination policy LRU policy Access delay

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