RGNE：粗糙粒化的网络嵌入式重叠社区发现方法

赵霞; 张泽华; 张晨威; 李娴

doi:10.7544/issn1000-1239.2020.20190572

RGNE：粗糙粒化的网络嵌入式重叠社区发现方法

¹(太原理工大学信息与计算机学院太原 030024)
²(伊利诺伊大学芝加哥分校计算机科学学院美国芝加哥 60607) (zhaoxiazzzz@163.com)

基金项目: 国家自然科学基金项目(61503273，61702356)；国家留学基金委项目(201806935047)

详细信息

中图分类号: TP181
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出版历程
- 发布日期: 2020-05-31

RGNE：A Network Embedding Method for Overlapping Community Detection Based on Rough Granulation

¹(College of Information and Computer, Taiyuan University of Technology, Taiyuan 030024)
²(School of Computer Science, University of Illinois at Chicago, Chicago, USA 60607)

Funds: This work was supported by the National Natural Science Foundation of China (61503273, 61702356) and the China Scholarship Council Program (201806935047).

摘要

摘要: 复杂网络社区挖掘作为近年的研究热点，重叠社区检测有重要的现实意义.传统社区发现方法将所有节点精确地划分到每一个子类中，形成非重叠划分.但硬划分方法较难处理含有不确定信息和噪声信息的复杂情况.而目前采用网络嵌入的方法进行重叠社区发现的研究较少，针对社区漂移和边界不确定的问题，提出了一种结合粗糙粒化的网络嵌入社区发现方法.通过网络嵌入获得融合结构信息和属性信息的节点表示，并将相似的节点映射到距离相近的低维连续的向量空间.然后，结合粗糙粒化的思想，考虑网络结构和节点上的多层次信息来处理社区边界上的不确定性区域，最终生成重叠社区.在网络公开数据集和人工数据集的实验结果都表明，提出的粗糙粒化的网络嵌入(network embedding based on rough granulation, RGNE)社区发现方法具有更高的精度，并可有效地处理不确定性网络的社区发现问题.最后，对影响实验效果的参数设置进行了详细讨论分析.
- 社区发现 /
- 重叠社区 /
- 社区漂移 /
- 网络嵌入 /
- 粗糙粒化
Abstract: Community mining of complex information networks is a research hotspot in recent years and the detection of overlapping communities has important practical significance. The traditional community detection method accurately divides all nodes into each subclass to form a non-overlapping partition. However, the hard partitioning method is more difficult to deal with complex situations involving uncertain information and noise information. At present, there are few researches on the method of network embedding for overlapping community detection. Aiming at the problems of community drift and boundary uncertainty, a network embedding community detection method based on rough granulation is proposed. The node representation of structure information and attribute information is obtained through network embedding, and the similar nodes are mapped to the low-dimensional continuous vector space with similar distances. Then, the network structure and multi-level information with rough granulation to deal with the uncertainty areas are considered, and overlapping communities are finally generated. The experimental results in network public datasets and synthetic datasets show that the RGNE(network embedding based on rough granulation)method has higher precision and can effectively deal with community detection problems of uncertain networks. Finally, the parameter settings affecting the experimental results are discussed and analyzed in detail.
- community detection /
- overlapping community /
- community drift /
- network embedding /
- rough granulation

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