A Graph Clustering Algorithm Based on Adaptive Neighbors Connectivity

This paper deals with graph clustering algorithm which partitions a set of vertices in graphs into smaller sets (clusters). Such vertices of the same set are related to each other rather than to those in the other sets. This means that most graph clustering algorithms are based on the topological shape or feature similarity. Nevertheless, these algorithms suffered from scalability because of the height computation requirements for similarity estimation. This paper represents a stimulus for the current study to introduce an algorithm that automatically finds the number of clusters based on shared neighbours among vertices. The study is based on the hypothesis that the proposed algorithm is able to efficiently find the graph clustering partitions for the whole graphs

Community detection in node-attributed social networks: a survey

Community detection is a fundamental problem in social network analysis consisting in unsupervised dividing social actors (nodes in a social graph) with certain social connections (edges in a social graph) into densely knitted and highly related groups with each group well separated from the others. Classical approaches for community detection usually deal only with network structure and ignore features of its nodes (called node attributes), although many real-world social networks provide additional actors' information such as interests. It is believed that the attributes may clarify and enrich the knowledge about the actors and give sense to the communities. This belief has motivated the progress in developing community detection methods that use both the structure and the attributes of network (i.e. deal with a node-attributed graph) to yield more informative and qualitative results. During the last decade many such methods based on different ideas have appeared. Although there exist partial overviews of them, a recent survey is a necessity as the growing number of the methods may cause repetitions in methodology and uncertainty in practice. In this paper we aim at describing and clarifying the overall situation in the field of community detection in node-attributed social networks. Namely, we perform an exhaustive search of known methods and propose a classification of them based on when and how structure and attributes are fused. We not only give a description of each class but also provide general technical ideas behind each method in the class. Furthermore, we pay attention to available information which methods outperform others and which datasets and quality measures are used for their evaluation. Basing on the information collected, we make conclusions on the current state of the field and disclose several problems that seem important to be resolved in future.Comment: This is an essentially revised version of the manuscrip