基于多Agent技術(shù)的異質(zhì)社會(huì)網(wǎng)絡(luò)群組形成方法研究
[Abstract]:Group formation is one of the hotspots in the current social network field, which allows a number of social individuals to achieve better performance performance. In order to accomplish the task successfully, group members not only need to have relevant professional skills, but also cooperate efficiently with each other in a team way. Therefore, a group of specialties are established. With the widespread use of online social software, social networks have brought great opportunities to the formation of professional, cooperative and efficient groups. With the popularity of mobile devices such as smartphones and iPad, thousands of active sites are active every time. Users, those with relevant professional skills, can help task requesters to build professional individual groups. On the other hand, active social relationships among users, such as trust relationships among friends and project cooperation among colleagues in the social network environment, can be used as effective cooperation among group members. Inspired by the above two advantages, this paper studies the formation of groups in the social network environment. However, social network groups are faced with some new problems due to the openness, autonomy and heterogeneity of social networks. On the one hand, from the perspective of the individual quality of society, (1) the behavior of the individual is heterogeneous, Some individuals may provide false private information, such as individuals may exaggerate their professional skills, social cooperation and work costs to improve their own work reward; (2) the objective of the individual is heterogeneous, in which the task requestor wants to establish a group of low working groups, but the individual chooses which group to join and What skills to provide to group tasks hope to maximize their own work reward. (3) the efficiency of cooperation between individuals is heterogeneous and dynamic, and the cooperation efficiency of the interconnected individuals is high and low, and the cooperation efficiency is not dynamic in the formation of group formation; on the other hand, the network structure is different. Qualitative angle, (4) the social network has the characteristics of community structure and the community attributes of the individual are heterogeneous. From the middle view of the social network, the social network is composed of several subnetworks with community structure, in which the individual relations in the community are close and the cooperation efficiency is high, and the individual relations between the communities are distant and close. In order to solve the new problems brought by the social individuals and network structure heterogeneity to the social network group, it includes (1) how to guarantee the personal information of the social individuals, and (2) how to meet the conflict between individuals. Standard, (3) how to ensure the robustness of groups in a social network environment with heterogeneous cooperative efficiency and dynamic cooperation efficiency. (4) how to improve the group efficiency by using the community structure characteristics of the social network. This paper uses the multi Agent technology to develop the corresponding social network group formation method research work, its main contribution can be returned. As follows: 1) taking into account the behavioral heterogeneity of individuals, the paper proposes an incentive mechanism based on the multi Agent game theory. The mechanism encourages individuals to provide private information honestly by paying their private information. The theoretical analysis and experimental results show that the mechanism ensures that each individual is honest with its personal information. In the state of information, it can maximize its own income, and the mechanism can be applied to large-scale social network groups to form the application of.2). Considering the heterogeneity of social individual goals, a group formation model based on multi Agent negotiation technology is proposed in this paper. The model is designed for different types of social individuals with different negotiation strategies. These strategies include which individual is hired by the task requester, the remuneration to be paid to the individual, which group is added to the individual and what skills to be provided to the group task. The theoretical analysis and experimental results show that the group formation based on the negotiation mechanism is proposed in comparison with the traditional group formation model. The model can form a more economical, professional, cooperative and efficient group.3 within a short time cost. Considering the heterogeneity and dynamic variability of cooperation efficiency among individuals, a distributed group formation method based on mobile Agent technology is proposed. This method can help each task skill to distribute a mobile Agent to help these At the same time, these mobile Agent can form alliances to achieve dynamic changes in adaptive network structure. The theoretical analysis shows that the proposed mobile Agent method can converge to the stable state in polynomial time, and the stable solution guarantees the efficient group composition and the group. The experimental results show that compared to the traditional group formation method for static network structure, the proposed method not only can find professional, cooperative and efficient, load balanced groups, but also can better adapt to the dynamic social network environment.4). This paper proposes a social network group formation model aware of community structure. This model allows individuals to cooperate with individuals in the community to form groups. In order to solve the problem of community-based group formation, a heuristic group formation algorithm is proposed in this paper. The algorithm makes full use of overlapping community individuals. The theoretical analysis shows that the heuristic group formation algorithm has a higher approximate degree on the social benefit index compared with the optimal algorithm, and proposes a sufficient condition to ensure the maximum social benefit of the algorithm. The experimental results show that compared to the traditional global network and the traditional network, the experimental results show that the algorithm is more efficient. Neighbor node group formation model, the community group formation model proposed in this paper can not only improve the efficiency of group cooperation, but also improve social benefits.
【學(xué)位授予單位】:東南大學(xué)
【學(xué)位級(jí)別】:博士
【學(xué)位授予年份】:2016
【分類號(hào)】:TP393.09;TP18
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