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Most existing KT models evaluate students' future performance centered on concepts, overlooking the differences between exercises containing the same concepts, thus affecting the models prediction accuracy. Moreover, in constructing the students' knowledge state, existing models fail to fully utilize the learning-forgetting features of students during the answering process, leading to an inaccurate modeling of students' knowledge states. To address these issues, this paper proposed an exercise embeddings and learning-forgetting features boosted knowledge tracing model. The model utilized the explicit relationships in the exercise-concept bipartite graph to calculate the implicit relationships within the graph, constructing an exerciseconcept relationship heterogeneous graph. To make full use of the rich relationship information in the heterogeneous graph, ELFBKT introduced a relational graph convolutional network (RGCN). Through the processing of RGCN, the model enhanced the quality of exercise embeddings and predicted students' future performance more accurately with an exercise-centric approach. Furthermore, ELFBKT fully utilized various learning-forgetting features to construct two gating-controlled mechanisms, modeling the students' learning and forgetting behaviors respectively, to more accurately model the students' knowledge states. Experiments on two real-world datasets show that ELFBKT outperforms other models in KT tasks. [ABSTRACT FROM AUTHOR]

Copyright of Journal of Central China Normal University is the property of Huazhong Normal University and its content may not be copied or emailed to multiple sites or posted to a listserv without the copyright holder's express written permission. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.)

The current State Grid communication and information networks rely on two separate network management systems.Such management structure brings challenges to network fault location,which calls for joint network fault analyze method. Grasping the transmission correlation of network nodes, a joint network fault location mechanism based on bipartite graph model was proposed.Firstly,network nodes were clustered into multiple sub-domains based on their fault correlation.Then,the bipartite graph fault correlation model was established for each sub-domain.Finally,the target sorting method was applied to locate network faults in every sub-domain simultaneously.Simulation experiments show that this method can locate network faults with high accuracy.

Copyright of Journal of Jilin University (Science Edition) / Jilin Daxue Xuebao (Lixue Ban) is the property of Zhongguo Xue shu qi Kan (Guang Pan Ban) Dian zi Za zhi She and its content may not be copied or emailed to multiple sites or posted to a listserv without the copyright holder's express written permission. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.)

In this paper, a logic algebraic algorithm in computing entire routes among the nodes of a communication network is improved, and a new algorithm of detecting cycles in bipartite graphs of LDPC codes is proposed. The algorithm obeys the logic algebraic calculation rules. Through integrating and deleting rows of relative matrix and correcting some elements of the matrix, the new algorithm can detect all the cycles in the bipartite graphs of LDPC codes. When the code length is n and information bits number is κ, the integrating and deleting algorithm needs (n - 1 ) x κ times and the correcting algorithm needs κ times. The validity and rationality of the algorithm are demonstrated in detail. The calculation course of the algorithm is shown by an example and the correctness is validated. [ABSTRACT FROM AUTHOR]

Copyright of Journal of Dalian University of Technology / Dalian Ligong Daxue Xuebao is the property of Journal of Dalian University of Technology and its content may not be copied or emailed to multiple sites or posted to a listserv without the copyright holder's express written permission. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.)

To address the problems of node heterogeneity and inapplicability of triangular form balance theory in signed bipartite network modeling, this paper proposed a signed bipartite graph neural network enhanced by potential group assignment and contrast learning, which could extract the display and implicit information fully through complementing each other with homogeneous and heterogeneous spaces. In the homogeneous space, this paper treated nodes as a combination of multiple learnable potential groups, and then mined information among nodes by training automatically. In the heterogeneous space, this paper adopted the attention aggregators with directions to aggregate information of neighbors, and then used the contrast learning for network reconstruction based on mutual information to guide the aggregation process to obtain more expressive node representations. This paper performed comparative experiments with a variety of related models on the signed link prediction task. Experimental results show that it can obtain optimal values for 12 of the 16 evaluation results obtained using four evaluation metrics on four real datasets, which verifies the effectiveness of the proposed model. [ABSTRACT FROM AUTHOR]

Copyright of Journal of Harbin University of Science & Technology is the property of Journal of Harbin University of Science & Technology and its content may not be copied or emailed to multiple sites or posted to a listserv without the copyright holder's express written permission. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.)

At present, network representation learning methods are mostly aimed at homogeneous networks, ignoring the particularity of attributed bipartite networks and the motifs structure of networks. In order to solve the above problems, this paper proposes an attributed bipartite graph neural network with motifs information for network representation learning (MABG). MABG adjusts the edge weights by the number of butterfly motifs formed by two nodes in the network, to construct the motifs weight matrix and obtain the attributed bipartite network adjacency matrix with motifs information. Then two different strategies are adopted to capture the explicit and implicit messages in the bipartite network. For explicit relationships, a message-passing mechanism is operated between different types of nodes. For implicit relationships, a message alignment mechanism is used in nodes of the same type. An adversarial model is implemented to minimize the difference between input attributes and explicit relationship representations. Finally, a cascaded framework is proposed to capture high-order network information and obtain the final node representation. Extensive experiments are conducted in recommended tasks on four real-world datasets. The results demonstrate the effectiveness of MABG compared with other state-of-art methods. [ABSTRACT FROM AUTHOR]

Copyright of Journal of Computer Engineering & Applications is the property of Beijing Journal of Computer Engineering & Applications Journal Co Ltd. and its content may not be copied or emailed to multiple sites or posted to a listserv without the copyright holder's express written permission. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.)

Online reviews play a significant role in users' purchasing decisions. In order to improve their reputation or degrade their competitors' products, some sellers employ a large numbers of review spammers to write fake reviews systematically and strategically to mislead potential consumers. In order to detect such organized spammer groups, this paper proposed a group spam detection algorithm that comprehensively considered the network structure and the behavior characteristics of reviewers. In implementation, this algorithm first obtained the closeness between reviewers based on the relevance of ratings and review time, and constructed a reviewer relationship graph. Secondly, based on the constructed reviewer relationship graph, it used label propagation method to detect the community and got a set of candidate groups. Finally, it restored the corresponding bipartite graphs of the candidate groups, and then found the final spammers on each bipartite graph by taking contrast suspiciousness as a metric. Experimental results based on real datasets demonstrate the effectiveness of the proposed algorithm. [ABSTRACT FROM AUTHOR]

In the C-V2 X environment, the high-speed movement of vehicles makes it impossible to obtain full CSI, and vehicles multiplexing of cellular resources causes interference to original network. This paper attempted to maximize the sum ergodic rate of system while ensuring reliability for V2 I link and V2 V link under partial CSI. It proposed the joint power control and channel multiplexing resource allocation algorithm. Firstly, according to the reliability constraint, it used geometric programming to analyze the feasible regions of power, and obtained the optimal power control for any single multiplexed pair. Then, the algorithm transformed channel multiplexing into a maximum weight bipartite graph matching problem by using the ergodic rate of the multiplexed pair as the weight of the bipartite graph and solved it by the KM algorithm. The simulation results show that, compared with other algorithms, the proposed resource allocation algorithm can optimize the resource allocation, effectively control the interference, and improve the sum ergodic rate of system while ensuring reliable communication for vehicles. [ABSTRACT FROM AUTHOR]

Representation learning of a user-item interaction network becomes an effective recommendation method. Most of the existing methods regard the interaction network as a static homogeneous network, ignoring the impact of interaction timing and node heterogeneity. In response to this problem, this paper proposed a recommendation method based on dynamic bipartite network representation learning. Firstly, the method constructed a time-series weighted bipartite network, and then respectively mapped user nodes and item nodes to different vector spaces to preserve the heterogeneity of the network, and aggregated the first-order and high-order neighbor information for center nodes with graph convolution. Finally it used a multi-layer perceptron to learn the nonlinear relationship between the two types of node embeddings and performed top-N recommendation. Experimental results on Amazon and Taobao datasets show that the proposed method is significantly superior than the related methods based on static or heterogeneous network representation learning in HR and NDCG indicators. [ABSTRACT FROM AUTHOR]

The second maximum value of the sum of squares of degrees of bipartite graphs with given number of vertices and fewer edges has been determined. The bipartite graphs with given number of ver-tices and fewer edges where the sum of squares of degrees attain the second maximum value are deter-mined in this paper by discussing the relation of the vertices and edges. It is useful for the research of the sum of squares of degrees for bipartite graphs. [ABSTRACT FROM AUTHOR]

Copyright of Journal of Central China Normal University is the property of Huazhong Normal University and its content may not be copied or emailed to multiple sites or posted to a listserv without the copyright holder's express written permission. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.)

Copyright of Journal of Chongqing University of Posts & Telecommunications (Natural Science Edition) is the property of Chongqing University of Posts & Telecommunications and its content may not be copied or emailed to multiple sites or posted to a listserv without the copyright holder's express written permission. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.)

Copyright of Journal of Computer Engineering & Applications is the property of Beijing Journal of Computer Engineering & Applications Journal Co Ltd. and its content may not be copied or emailed to multiple sites or posted to a listserv without the copyright holder's express written permission. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.)

Most recommendation systems based on the network structure suffer from lack of diversity, so that the paper proposed a recommendation system of big data based on multi-weight projection of bipartite network. Firstly, it abstracted the basic information of datasets, and applied items-users lists as an input to Levenshtein distance algorithm to compute similarity of each property. Then, it computed the number of common neighbors of the nodes in the bipartite network, the degree of common neighbors of the nodes in the bipartite network and degree of each node in bipartite network, and also computed the triple weights of each side of the bipartite network. Lastly, it adopted the enhanced bipartite projection technique to abstract the potential links of the bipartite network to realize the link prediction based on similarity. It realized the experiments based on both of big dataset and small dataset. The results show that the proposed algorithm outperforms different kinds of recommendation systems in terms of accuracy and coverage of recommendation, at the same time, it outperforms the other recommendation system based on network structure. [ABSTRACT FROM AUTHOR]

Copyright of Journal of Jilin University (Science Edition) / Jilin Daxue Xuebao (Lixue Ban) is the property of Zhongguo Xue shu qi Kan (Guang Pan Ban) Dian zi Za zhi She and its content may not be copied or emailed to multiple sites or posted to a listserv without the copyright holder's express written permission. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.)

Copyright of Application Research of Computers / Jisuanji Yingyong Yanjiu is the property of Application Research of Computers Edition and its content may not be copied or emailed to multiple sites or posted to a listserv without the copyright holder's express written permission. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.)

Copyright of Control Theory & Applications / Kongzhi Lilun Yu Yinyong is the property of Editorial Department of Control Theory & Applications and its content may not be copied or emailed to multiple sites or posted to a listserv without the copyright holder's express written permission. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.)

Copyright of Journal of South China Normal University (Natural Science Edition) / Huanan Shifan Daxue Xuebao (Ziran Kexue Ban) is the property of Journal of South China Normal University (Natural Science Edition) Editorial Office and its content may not be copied or emailed to multiple sites or posted to a listserv without the copyright holder's express written permission. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.)

Copyright of Journal of Jilin University (Science Edition) / Jilin Daxue Xuebao (Lixue Ban) is the property of Zhongguo Xue shu qi Kan (Guang Pan Ban) Dian zi Za zhi She and its content may not be copied or emailed to multiple sites or posted to a listserv without the copyright holder's express written permission. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.)

Copyright of Journal of Jilin University (Science Edition) / Jilin Daxue Xuebao (Lixue Ban) is the property of Zhongguo Xue shu qi Kan (Guang Pan Ban) Dian zi Za zhi She and its content may not be copied or emailed to multiple sites or posted to a listserv without the copyright holder's express written permission. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.)

Copyright of Control Theory & Applications / Kongzhi Lilun Yu Yinyong is the property of Editorial Department of Control Theory & Applications and its content may not be copied or emailed to multiple sites or posted to a listserv without the copyright holder's express written permission. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.)

Copyright of Chinese Journal of Applied Entomology is the property of Chinese Journal of Applied Entomology, Editorial Department and its content may not be copied or emailed to multiple sites or posted to a listserv without the copyright holder's express written permission. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.)

Aiming at the unreasonable allocation of resource in the recommendation algorithm of bipartite network, and in order to enrich the diversity of recommended results and promote the recommendation of unpopular items, this paper proposed the algorithm for bipartite graph network structure, which used differential path weight to change resource delivery. It changed the first stage of resource transfer rules by user similarity to construct path weights and made the resources flow more towards the user nodes similar to the target users. Through the consideration of constructing path weight of the articles attributes’ similarity, the second stage resources were more likely to flow to the objects which had similar attributes with the target users’ purchased products. Experimental results show that the proposed algorithm improves the combination property of recommendation, and better solves the related problems in recommendation, which outperforms other popular network structure algorithms. [ABSTRACT FROM AUTHOR]

In 6G, one of the important application scenarios is ultra-massive machinetype communications, which is mainly oriented to the application scenarios of concurrent access of massive terminals such as the satellite Internet of Things.How to design an access mechanism that can meet the concurrent access service requirements of large-scale users in machine communication scenarios for the future 6G satellite communication network is an important problem to be solved.A random access protocol based on intra-frame interference cancellation was proposed.In this protocol, each access user who needs to transmit data sends multiple packet copies in one frame, and the receiver can successfully receive packets from multiple users in confl icting time slots by using interference cancellation algorithms in the frame.Compared with the traditional random access mechanism, this mechanism can greatly improve the throughput performance in the scenario of multi-user concurrent access.

Copyright of Control Theory & Applications / Kongzhi Lilun Yu Yinyong is the property of Editorial Department of Control Theory & Applications and its content may not be copied or emailed to multiple sites or posted to a listserv without the copyright holder's express written permission. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.)

The efficiency of traditional user-based collaborative filtering (user-based CF) recommendation algorithm is reduced with data increasing. This paper proposes a recommendation algorithm based on community detection (RACD) in bipartite networks by introducing bipartite network community detection theory into user-based CF recommendation algorithm. Firstly， the user-item rating matrix is mapped into user-item bipartite network. Then， the community information of each user is obtained by using RACD to divide the user-item network. Finally， the items are recommended to the target user according to other users in the same community. Experiments on real-world classic network datasets show that the RACD can effectively improve real-time recommendation efficiency of the recommendation system. [ABSTRACT FROM AUTHOR]

Copyright of Operations Research Transactions / Yunchouxue Xuebao is the property of Editorial office of Operations Research Transactions and its content may not be copied or emailed to multiple sites or posted to a listserv without the copyright holder's express written permission. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.)

In order to solve the graph convolution-encoder weight sharing and cannot distinguish the importance between neighbors in the process of extracting user and item information, graph neural network method cannot show capturing the non- local context information of the knowledge graph (KG) in the process of using the KG as auxiliary information, this paper proposes graph attention encoder framework based on bi-directional interactive graph pass, which displays the local and non-local context information of the KG. First, the embedding vector of the user and the item is obtained by the graph attention encoder. Second, the algorithm captures the local context information of the KG through the user-specific graph attention mechanism. In addition, random walk sampling is used to extract the non-local context of the entity and recurrent neural network is used to model the dependency between the entity and the non-local context entity. Finally, the links in the bipartite graph are reconstructed by bi-linear decoder. Compared with existing methods, the experimental results on real datasets verify the superiority of this model. [ABSTRACT FROM AUTHOR]

Copyright of Application Research of Computers / Jisuanji Yingyong Yanjiu is the property of Application Research of Computers Edition and its content may not be copied or emailed to multiple sites or posted to a listserv without the copyright holder's express written permission. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.)

Copyright of Control Theory & Applications / Kongzhi Lilun Yu Yinyong is the property of Editorial Department of Control Theory & Applications and its content may not be copied or emailed to multiple sites or posted to a listserv without the copyright holder's express written permission. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.)

The second largest value of the sum of squares of degrees with a given number of vertices and few edges for bipartite graphs is studyed. After determining the bipartite graphs with few edges where the sum of the squares of degrees is maximum, the second largest value is determined by comparing the range of the vertices and edges. It is useful for the research of the sum of squares of degrees for bipartite graphs. [ABSTRACT FROM AUTHOR]

Copyright of Geomatics & Information Science of Wuhan University is the property of Geomatics & Information Science of Wuhan University and its content may not be copied or emailed to multiple sites or posted to a listserv without the copyright holder's express written permission. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.)

Copyright of Journal of Jiangsu University (Natural Science Edition) / Jiangsu Daxue Xuebao (Ziran Kexue Ban) is the property of Editorial Department of Journal of Jiangsu University (Natural Science Edition) and its content may not be copied or emailed to multiple sites or posted to a listserv without the copyright holder's express written permission. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.)

Copyright of Application Research of Computers / Jisuanji Yingyong Yanjiu is the property of Application Research of Computers Edition and its content may not be copied or emailed to multiple sites or posted to a listserv without the copyright holder's express written permission. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.)

Copyright of Application Research of Computers / Jisuanji Yingyong Yanjiu is the property of Application Research of Computers Edition and its content may not be copied or emailed to multiple sites or posted to a listserv without the copyright holder's express written permission. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.)

Copyright of Application Research of Computers / Jisuanji Yingyong Yanjiu is the property of Application Research of Computers Edition and its content may not be copied or emailed to multiple sites or posted to a listserv without the copyright holder's express written permission. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.)

Copyright of Application Research of Computers / Jisuanji Yingyong Yanjiu is the property of Application Research of Computers Edition and its content may not be copied or emailed to multiple sites or posted to a listserv without the copyright holder's express written permission. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.)

Copyright of Application Research of Computers / Jisuanji Yingyong Yanjiu is the property of Application Research of Computers Edition and its content may not be copied or emailed to multiple sites or posted to a listserv without the copyright holder's express written permission. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.)

Copyright of Application Research of Computers / Jisuanji Yingyong Yanjiu is the property of Application Research of Computers Edition and its content may not be copied or emailed to multiple sites or posted to a listserv without the copyright holder's express written permission. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.)

Copyright of Journal of Chongqing University of Posts & Telecommunications (Natural Science Edition) is the property of Chongqing University of Posts & Telecommunications and its content may not be copied or emailed to multiple sites or posted to a listserv without the copyright holder's express written permission. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.)

Copyright of Application Research of Computers / Jisuanji Yingyong Yanjiu is the property of Application Research of Computers Edition and its content may not be copied or emailed to multiple sites or posted to a listserv without the copyright holder's express written permission. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.)

Copyright of Application Research of Computers / Jisuanji Yingyong Yanjiu is the property of Application Research of Computers Edition and its content may not be copied or emailed to multiple sites or posted to a listserv without the copyright holder's express written permission. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.)

Copyright of Application Research of Computers / Jisuanji Yingyong Yanjiu is the property of Application Research of Computers Edition and its content may not be copied or emailed to multiple sites or posted to a listserv without the copyright holder's express written permission. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.)

Copyright of Journal of Jiangsu University (Natural Science Edition) / Jiangsu Daxue Xuebao (Ziran Kexue Ban) is the property of Editorial Department of Journal of Jiangsu University (Natural Science Edition) and its content may not be copied or emailed to multiple sites or posted to a listserv without the copyright holder's express written permission. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.)

To tackle the network congestion issue caused by redundant traffic by users' repeated access to the same network resources, this paper proposed a weighted bipartite network (WBN) model which evolved with time. Through the analysis of users' network behavior and redundant traffic' formation and evolution mechanism, WBN model adopted ways of preferential connection and topology growth to complete network evolution. Simulation experiment results show that strength of user nodes has experienced from the exponential distribution to power-law distribution and then random distribution. It effectively simulates the evolution of real network redundant traffic. Simulation results are also consistent with theoretical analysis and show that WBN model is accurate and practical. [ABSTRACT FROM AUTHOR]

The external factor plays an important role in affecting user's collective behaviors. However, the external factor from the viewpoint of theoretical model to analyze user's collective behaviors is missing. Thus this paper analyzed the structural property of user-object bipartite networks from a theoretical model. By taking into account the external factors and intrinsic attributes, with the help of generation function, it developed a theoretical model to derivate both the degree distributions of the user and object as well as their unipartite projections. Further, this paper gave an example and numerical simulations. The results show that the results of numerical simulation are consistent with the empirical ones very well, which suggests that it is feasible to analyze structural property of bipartite networks in terms of external factors and intrinsic attributes. [ABSTRACT FROM AUTHOR]