Network Collaborator：网络重构与社区检测之间的知识迁移

MLP增强的非负张量RESCAL分解用于动态社区检测

MLP-Enhanced Nonnegative Tensor RESCAL Decomposition for Dynamic Community Detection

本文提出MLP-NTD模型，通过引入MLP解耦分解秩与社区数量，提升动态社区检测的准确性和鲁棒性。

Community detection in sparse time-evolving graphs with a dynamical Bethe-Hessian

This article considers the problem of community detection in sparse dynamical graphs in which the community structure evolves over time. A fast spectral algorithm based on an extension of the Bethe-Hessian matrix is proposed, which benefits from the positive correlation in the class labels and in their temporal evolution and is designed to be applicable to any dynamical graph with a community structure. Under the dynamical degree-corrected stochastic block model, in the case of two classes of equal size, we demonstrate and support with extensive simulations that our proposed algorithm is capab…

META-CODE: Community Detection via Exploratory Learning in Topologically Unknown Networks

The discovery of community structures in social networks has gained considerable attention as a fundamental problem for various network analysis tasks. However, due to privacy concerns or access restrictions, the network structure is often unknown, thereby rendering established community detection approaches ineffective without costly data acquisition. To tackle this challenge, we present META-CODE, a novel end-to-end solution for detecting overlapping communities in networks with unknown topology via exploratory learning aided by easy-to-collect node metadata. Specifically, META-CODE consists…

High-order Order Proximity-Incorporated, Symmetry and Graph-Regularized Nonnegative Matrix Factorization for Community Detection

Community describes the functional mechanism of a network, making community detection serve as a fundamental graph tool for various real applications like discovery of social circle. To date, a Symmetric and Non-negative Matrix Factorization (SNMF) model has been frequently adopted to address this issue owing to its high interpretability and scalability. However, most existing SNMF-based community detection methods neglect the high-order connection patterns in a network. Motivated by this discovery, in this paper, we propose a High-Order Proximity (HOP)-incorporated, Symmetry and Graph-regular…

Mixed-SCORE+ for mixed membership community detection

Mixed-SCORE is a recent approach for mixed membership community detection proposed by Jin et al. (2017) which is an extension of SCORE (Jin, 2015). In the note Jin et al. (2018), the authors propose SCORE+ as an improvement of SCORE to handle with weak signal networks. In this paper, we propose a method called Mixed-SCORE+ designed based on the Mixed-SCORE and SCORE+, therefore Mixed-SCORE+ inherits nice properties of both Mixed-SCORE and SCORE+. In the proposed method, we consider K+1 eigenvectors when there are K communities to detect weak signal networks. And we also construct vertices hunt…

ComHapDet: A Spatial Community Detection Algorithm for Haplotype Assembly

Background: Haplotypes, the ordered lists of single nucleotide variations that distinguish chromosomal sequences from their homologous pairs, may reveal an individual's susceptibility to hereditary and complex diseases and affect how our bodies respond to therapeutic drugs. Reconstructing haplotypes of an individual from short sequencing reads is an NP-hard problem that becomes even more challenging in the case of polyploids. While increasing lengths of sequencing reads and insert sizes {\color{black} helps improve accuracy of reconstruction}, it also exacerbates computational complexity of th…

扩散诱导的空间注意力重叠社区检测

Diffusion-Induced Spatial Attention Overlapping Community Detection

针对现有图神经网络在重叠社区检测中因局部消息传递导致的边界模糊和长程依赖缺失问题，提出DISCO框架，融合扩散结构先验、稀疏注意力与非负社区学习，在基准上表现优异，并在网络安全异常检测中验证了实用性。

DAG：面向属性图的深度自适应生成式无 $K$ 社区检测

DAG: Deep Adaptive and Generative $K$-Free Community Detection on Attributed Graphs

针对属性图社区数量 $K$ 未知的问题，提出端到端深度自适应生成模型 DAG，联合检测社区并搜索社区数，且通过无标签指标 EDGE 和游戏数据验证其优越性。

Modularity-Aware Graph Autoencoders for Joint Community Detection and Link Prediction

Graph autoencoders (GAE) and variational graph autoencoders (VGAE) emerged as powerful methods for link prediction. Their performances are less impressive on community detection problems where, according to recent and concurring experimental evaluations, they are often outperformed by simpler alternatives such as the Louvain method. It is currently still unclear to which extent one can improve community detection with GAE and VGAE, especially in the absence of node features. It is moreover uncertain whether one could do so while simultaneously preserving good performances on link prediction. I…

Multivariate Spatiotemporal Hawkes Processes and Network Reconstruction

There is often latent network structure in spatial and temporal data and the tools of network analysis can yield fascinating insights into such data. In this paper, we develop a nonparametric method for network reconstruction from spatiotemporal data sets using multivariate Hawkes processes. In contrast to prior work on network reconstruction with point-process models, which has often focused on exclusively temporal information, our approach uses both temporal and spatial information and does not assume a specific parametric form of network dynamics. This leads to an effective way of recoverin…

拓扑不确定性下探索式学习辅助社区发现的统一框架

A Unified Framework for Exploratory Learning-Aided Community Detection Under Topological Uncertainty

本文提出统一框架 META-CODE，在网络拓扑未知或部分已知时，结合节点元数据、图神经网络嵌入、节点查询探索和孪生网络推断，迭代检测重叠社区，并在多个真实数据集上验证了其显著优于基准方法。