Social Networks is an essential phenomenon in all aspects through various perspectives. These networks contain a large number of users better termed as nodes and the connections between the users termed as edges. For efficient information processing and retrieving, accessing the influential node isessential for improving the diffusion process. To identify the influential node inside a heterogeneous community, incorporating probability metrics with regression classifier is put forth stated by proposed method Support Vector Bayesian Machine (SVBM). Node metrics such as degree centrality, closeness centrality is measured for eliminating the nodes primarily. A standardized index based on the centrality values computed for enhancing into SVBM. After the standardized index, similarity dissimilarity index values evaluated by combining the Euclidean, Hamming, Pearson coefficient for valued relations and Jaccard for binary relations which results in a single index value considered as the power degree value(p). The value p determines the node’s boundedness, which indicates the range of influence within the community. The outlier nodes in the bounded region get eliminated, and the nodes remaining taken for the final phase of SVBM, probability regression line predicts the node inhibiting the most influential nature. Experimental evaluation of the proposed system with the existing Support Vector Machine (SVM) technique resulted in 0.95 and 0.41 respectively for Area Under Curve (AUC) denoting that the true positive influential node classification process from the other existing nodes was higher than SVM. In comparison with the existing SVM, the proposed methodology SVBM attained a node detection, which influenced a higher diffusion rate within the networks.

中文翻译：

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社交网络中影响节点预测的混合节点分类机制

从各个角度来看，社交网络在各个方面都是必不可少的现象。这些网络包含大量用户（最好称为节点）和用户之间的连接（称为边缘）。为了有效地进行信息处理和检索，访问有影响力的节点对于改进扩散过程至关重要。为了识别异构社区内部的影响节点，提出了将概率指标与回归分类器结合起来的方法，该方法通过支持向量贝叶斯机（SVBM）提出。测量诸如度中心度，紧密度中心度之类的节点度量以主要消除节点。基于为增强为SVBM而计算的中心值的标准化索引。在标准化指标之后，通过结合欧几里得来评估相似性相异性指标值，Hamming，Pearson系数用于值关系，Jaccard用于二元关系，这将单个索引值视为幂度值（p）。值p确定节点的有界度，该界度指示社区内的影响范围。边界区域中的离群节点被消除，剩余的节点被用于SVBM的最后阶段，概率回归线预测该节点抑制了最有影响的性质。使用现有的支持向量机（SVM）技术对该系统进行的实验评估分别得出曲线下面积（AUC）为0.95和0.41，这表明来自其他现有节点的真实正影响节点分类过程高于SVM。与现有的SVM相比，拟议的SVBM方法实现了节点检测，