As the size of available networks is continuously increasing (even with millions of nodes), large-scale complex networks are receiving significant attention. While existing overlapping-community detection algorithms are quite effective in analyzing complex networks, most of these algorithms suffer from scalability issues when applied to large-scale complex networks, which can have more than 1,000,000 nodes. To address this problem, we propose an efficient local-expansion-based overlapping-community detection algorithm using local-neighborhood information (OCLN). During the iterative expansion process, only neighbors of nodes added in the last iteration (rather than all neighbors) are considered to determine whether they can join the community. This significantly reduces the computational cost and enhances the scalability for community detection in large-scale networks. A belonging coefficient is also proposed in OCLN to filter out incorrectly identified nodes. Theoretical analysis demonstrates that the computational complexity of the proposed OCLN is linear with respect to the size of the network to be detected. Experiments on large-scale LFR benchmark and real-world networks indicate the effectiveness of OCLN for overlapping-community detection in large-scale networks, in terms of both computational efficiency and detected-community quality.

中文翻译：

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大规模复杂网络中基于局域信息的重叠社区检测算法

随着可用网络规模的不断增加（即使有数百万个节点），大规模的复杂网络也受到了极大的关注。尽管现有的重叠社区检测算法在分析复杂网络方面非常有效，但是当应用于具有超过1,000,000个节点的大规模复杂网络时，这些算法中的大多数都会遇到可伸缩性问题。为了解决这个问题，我们提出了一种使用局部邻居信息（OCLN）的高效的基于局部扩展的重叠社区检测算法。在迭代扩展过程中，仅考虑在上一次迭代中添加的节点的邻居（而不是所有邻居）来确定它们是否可以加入社区。这显着降低了计算成本，并增强了大规模网络中社区检测的可伸缩性。在OCLN中还提出了一个归属系数，以滤除错误识别的节点。理论分析表明，提出的OCLN的计算复杂度相对于要检测的网络大小是线性的。在大型LFR基准和实际网络上进行的实验表明，在计算效率和检测到的社区质量方面，OCLN对于大型网络中的重叠社区检测都是有效的。理论分析表明，提出的OCLN的计算复杂度相对于要检测的网络大小是线性的。在大型LFR基准和实际网络上进行的实验表明，在计算效率和检测到的社区质量方面，OCLN对于大型网络中的重叠社区检测都是有效的。理论分析表明，提出的OCLN的计算复杂度相对于要检测的网络大小是线性的。在大型LFR基准和实际网络上进行的实验表明，在计算效率和检测到的社区质量方面，OCLN对于大型网络中的重叠社区检测都是有效的。