社交网络中基于K核分解的意见领袖识别算法

针对在社交网络中挖掘意见领袖时存在的计算复杂度高的难题，提出了一种基于K核分解的意见领袖识别算法CR。首先，基于K核分解方法获取社交网络中的意见领袖候选集，以缩小识别意见领袖的数据规模；然后，提出包括位置相似性和邻居相似性的用户相似性的概念，利用K核值、入度数、平均K核变化率和用户追随者个数计算用户相似性，并根据用户相似性对候选集中的用户计算全局影响力；最后，根据用户全局影响力对意见领袖候选集中的用户进行排序，从而识别意见领袖。在实验部分使用独立级联模型（ICM）预测的用户影响力和中心性两种评价指标在三个大小不同的真实数据集上对所提算法选出的意见领袖集进行评估，并将该算法与其他三种识别意见领袖的算法对比，结果表明该算法选出的影响力Top-15的用户平均影响力以21.442高于其他三个算法。另外，与四种与K核相关的算法做相关性指标对比的结果表明，CandidateRank算法总体来说效果较好。综上，CandidateRank算法在降低计算复杂度的同时提高了准确性。

Opinion leader recognition algorithm based on K-core decomposition in social networks

In view of the high computational complexity of opinion leader mining in social networks， an opinion leader recognition algorithm based on K-core decomposition， named CandidateRank （CR）， was proposed. Firstly， the opinion leader candidate set in a social network was obtained based on K-core decomposition method， so as to reduce the data size of opinion leader recognition. Then， a user similarity concept including location similarity and neighbor similarity was proposed， and the user similarity was calculated by K-core value， the number of entries， average K-core change rate and the number of user followers， and the global influence of the user in the candidate set was calculated according to the user similarity. Finally， opinion leaders were recognized by ranking users in the opinion leader candidate set by the global influence. In the experiment， two evaluation indexes of user influence predicted by Independent Cascade Model （ICM） and centrality were used to evaluate the opinion leader set selected by the proposed algorithm on three real datasets with different sizes. The results show that the proposed algorithm has the average user influence for the selected Top-15 users of 21.442， which is higher than those of the other three algorithms. In addition， compared to four K-core-related algorithms in correlation index， the results show that CandidateRank algorithm performs better in general. In summary， CandidateRank algorithm improves the accuracy while reducing the computational complexity.