计算机科学 ›› 2021, Vol. 48 ›› Issue (11A): 198-203.doi: 10.11896/jsjkx.210200113
潘雨1,2, 邹军华1, 王帅辉3, 胡谷雨1, 潘志松1
PAN Yu1,2, ZOU Jun-hua1, WANG Shuai-hui3, HU Gu-yu1, PAN Zhi-song1
摘要： 挖掘复杂网络中的社团结构有助于理解网络内部结构和功能特性,具有重要的理论价值和实际应用意义。随着信息技术的飞速发展,爆炸式增长的网络数据为社团发现任务提出了前所未有的挑战。为此,文中利用深度神经网络将网络表示学习和社团发现领域相连接,提出一种基于网络表示学习的深度社团发现方法。算法首先根据节点潜在的社团成员相似性来量化节点之间的结构相似度,从而构造包含潜在社团结构信息的社团结构矩阵;然后建立由多个非线性函数组成的多层自编码器,将社团结构矩阵作为深度自编码器的输入,获得保存了潜在社团结构的节点低维表示;最后在网络表示上应用K-means聚类策略获得社团结构。在不同规模的真实网络和人工网络上进行了大量的实验,并与典型的算法进行比较,实验结果表明了算法的可行性和有效性。
中图分类号:
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