Traditional community detection methods in attributed networks (eg, social network) usually disregard abundant node attribute information and only focus on structural information of a graph. Existing community detection methods in attributed networks are mostly applied in the detection of nonoverlapping communities and cannot be directly used to detect the overlapping structures. This article proposes an overlapping community detection algorithm in attributed networks. First, we employ the modified X-means algorithm to cluster attributes to form different themes. Second, we employ the label propagation algorithm (LPA), which is based on neighborhood network conductance for priority and the rule of theme weight, to detect communities in each theme. Finally, we perform redundant processing to form the final community division. The proposed algorithm improves the X-means algorithm to avoid the effects of outliers. Problems of LPA such as instability of division and adjacent communities being easily merged can be corrected by prioritizing the node neighborhood network conductance. As the community is detected in the attribute subspace, the algorithm can find overlapping communities. Experimental results on real-attributed and synthetic-attributed networks show 484

In order to accurately understand the economic development of enterprises and increase the company’s economic benefits, a study on financial forecasting and decision-making in big data cloud accounting enterprises is proposed. Enterprises improve the efficiency of data utilization by acquiring information processing and analysis, establishing a diversified control mechanism, and improving the effectiveness of financial and tax management. The objective function is optimized using a structured sparse induced parametric number to calculate the data block centers to describe the data objects more comprehensively and make the obtained clustered financial results more accurate. Adding classifiers to the set of labeled samples and constraining the joined samples belonging to the wrong class combine multiple kernels from different perspectives to obtain a comprehensive measure of similarity. Selecting sub-kernel functions and parameters to construct multiple kernel functions, the learning and generalization capabilities of kernel functions, and using high-dimensional data feature vectors to construct a shared hidden subspace to maximize the similarity between prediction samples and assign greater weights in the multi-perspective clustering process for corporate financial forecasting and decision making. The analysis results show that using data clustering cloud finance, financial data can be collected and corrected promptly, and the budget accuracy is up to 90%, which provides important help to enterprise financial decision-making.