Graph partitioning, or community detection, has been widely investigated in network science. Yet, the correct structure on a given is essentially data-driven. Thus, instead of formal definition, diverse measures have conceived to capture intuitive desirable properties shared by most structures. In this work, we propose preprocessing based doubly stochastic scaling adjacency matrices, highlight ...

Community detection in network data aims at grouping similar nodes sharing certain characteristics together. Most existing methods focus on detecting communities undirected networks, where simil...

This paper surveys recent theoretical advances in convex optimization approaches for community detection. We introduce some important techniques and results establishing the consistency of detection under various statistical models. In particular, we discuss basic based on primal dual analysis. also present that demonstrate several distinctive advantages detection, including robustness against ...

It has become a tendency to use combination of autoencoders and graph neural networks for attribute clustering solve the community detection problem. However, existing methods do not consider influence differences between node neighborhood information high-order information, fusion structural features is insufficient. In order make better we propose model named fusing attention network (CDFG). ...

Community structure detection is of great importance because it can help in discovering the relationship between the function and the topology structure of a network. Many community detection algorithms have been proposed, but how to incorporate the prior knowledge in the detection process remains a challenging problem. In this paper, we propose a semi-supervised community detection algorithm, ...

Though much work has been done on ensemble clustering in data mining, the application of ensemble methods to community detection in networks is in its infancy. In this paper, we propose two ensemble methods: EnDisCo and MeDOC++. EnDisCo performs disjoint community detection. In contrast, MeDOC++ performs disjoint, overlapping, and fuzzy community detection and represents the first ever ensemble...

Four major factors govern the intricacies of community extraction in networks: (1) the literature offers a multitude of disparate community detection algorithms whose output exhibits high structural variability across the collection, (2) communities identified by algorithms may differ structurally from real communities that arise in practice, (3) there is no consensus characterizing how to disc...