As most existing enterprise community discovery algorithms focus on homogenous market environment, without reflecting the participation of some enterprises in multiple supply chain operations, a core community representation model based on node mapping relationship, Map-Community, was proposed. By constructing two different role nodes and their different mapping relationships, the ownership community of a enterprise was determined. Based on this representation model, Node Mapping Algorithm (NMA) with approximately-linear time-space complexity was proposed. Firstly, filtering operation was used to obtain the biconnected core graph in the topology diagram of the supply chain network. Secondly, mapping degree was introduced to select the core enterprise nodes. Thirdly, local expansion was performed according to the mapping judgment rules. Finally, the local community structure was extended to the global network by backtracking and overlapping areas were discovered. In the LFR (Lancichinetti-Fortunato-Radicchi) network application experiment, NMA shows low sensitivity to threshold change and is superior to LFM (Local Fitness Maximization), COPRA (Community Overlap PRopagation Algorithm) and GCE (Greedy Clique Expansion) in terms of practicality. Simulation was carried out in the enterprise social network, and the meaning of distribution effect was summarized by the community division. The experimental results verify the feasibility of this algorithm for overlapping enterprise community discovery and its performance advantages in discovery quality.

Concerning the problem about the identification and evolution of enterprise trust alliance in dynamic trust network, a Graph Clustering (GC) algorithm based on fragment was proposed. Firstly, by considering the time information of network evolution, the trust network of enterprise was encoded. Secondly, the evaluation function of coding cost for dividing and presenting the structure of trust network was built. When the trust alliance was stable, the trust network during this time period would be compressed into a fragment; when the alliance changed, a new trust network fragment would be built and the structure of it would be re-devided. Finally, by finding the minimum coding cost, the stable structure of trust alliance and the timestamp of structural mutation could be found. The experimental results indicate that the proposed algorithm can identify the enterprise trust alliances and their mutations; and the accuracy and operating efficiency are higher than the classical community discovery algorithm.

In allusion to the problem of cold start technology and data scarcity in the traditional collaborative intelligence recommendation technology, in order to improve the efficiency and accuracy of recommendation algorithm, multidimensional collaborative intelligence recommendation based on social media context was proposed. In this model, the feature attributes and behavioral characteristics of the target users were considered into the information of social media context, users' interests in different social media context were dynamically captured in real-time, and OnLine Analytical Processing (OLAP) technology was used to process multidimensional data. The social relationship between users and the political and economic environment were regarded as an important indicator, then, the similarity between users were calculated using Pearson coefficient and cloud model, to get personalized and customized recommendation results. The experimental results show that the average absolute error of the model is significantly less than traditional collaborative intelligent recommendation and simple recommendation technology based on cloud model.

In order to solve the low trustiness problem in the supply chain cooperation process, in the decision making condition with unknown attribute and time weights, a reputation-based multi-attribute and multi-period supply chain partner selection model was proposed. Triangular fuzzy numbers were introduced to describe the appraisal information in language categories. Attribute and time weights for each period were determined by the grey correlations among different attributes and an improved time decay function respectively. Punishing variation weight method was introduced in the time weights setting, allowing time weights for each period to vary with the reputation values. The experimental results show that the model is able to select preferable partners whose reputations are well-balanced over different periods. The model has flexibility and can be adapted to different requirements in partner selection.

The members of information production supply chain face different risks. The interval-valued fuzzy Shapley value method was proposed to calculate the distribution of profit to realize fairness. Under the condition of income uncertainty, the fuzzy profit values returns were built and a membership function of interval-valued fuzzy Shapley was introduced. A certain allocation decision was presented. Considering the impact of various risk factors on the distribution of profits, the Fuzzy Analytic Hierarchy Process (AHP) method was adopted to revise the risk factors to ensure the stability of the supply chain of information products.