A Testing Based Extraction Algorithm for Identifying Significant Communities in Networks
نویسندگان
چکیده
A common and important problem arising in the study of networks is how to divide the vertices of a given network into one or more groups, called communities, in such a way that vertices of the same community are more interconnected than vertices belonging to different ones. We propose and investigate a testing based community detection procedure called Extraction of Statistically Significant Communities (ESSC). The ESSC procedure is based on p-values for the strength of connection between a single vertex and a set of vertices under a reference distribution derived from a conditional configuration network model. The procedure automatically selects both the number of communities in the network, and their size. Moreover, ESSC can handle overlapping communities and, unlike the majority of existing methods, identifies “background” vertices that do not belong to a well-defined community. The method has only one parameter, which controls the stringency of the hypothesis tests. We investigate the performance and potential use of ESSC, and compare it with a number of existing methods, through a validation study using four real network datasets. In addition, we carry out a simulation study to assess the effectiveness of ESSC in networks with various types of community structure including networks with overlapping communities and those with background vertices. These results suggest that ESSC is an effective exploratory tool for the discovery of relevent community structure in complex network systems. Data and software are available at http://www.unc.edu/~jameswd/research.html.
منابع مشابه
Identifying overlapping communities using multi-agent collective intelligence
The proposed algorithm in this research is based on the multi-agent particle swarm optimization as a collective intelligence due to the connection between several simple components which enables them to regulate their behavior and relationships with the rest of the group according to certain rules. As a result, self-organizing in collective activities can be seen. Community structure is crucial...
متن کاملA Multiagent Reinforcement Learning algorithm to solve the Community Detection Problem
Community detection is a challenging optimization problem that consists of searching for communities that belong to a network under the assumption that the nodes of the same community share properties that enable the detection of new characteristics or functional relationships in the network. Although there are many algorithms developed for community detection, most of them are unsuitable when ...
متن کاملMining Overlapping Communities in Real-world Networks Based on Extended Modularity Gain
Detecting communities plays a vital role in studying group level patterns of a social network and it can be helpful in developing several recommendation systems such as movie recommendation, book recommendation, friend recommendation and so on. Most of the community detection algorithms can detect disjoint communities only, but in the real time scenario, a node can be a member of more than one ...
متن کاملDetecting communities of workforces for the multi-skill resource-constrained project scheduling problem: A dandelion solution approach
This paper proposes a new mixed-integer model for the multi-skill resource-constrained project scheduling problem (MSRCPSP). The interactions between workers are represented as undirected networks. Therefore, for each required skill, an undirected network is formed which shows the relations of human resources. In this paper, community detection in networks is used to find the most compatible wo...
متن کاملDeterminants for Entrepreneurial Behavior among Members of Virtual Agricultural Social Networks
Entrepreneurship is an important issue that has been raised in all aspects of economic and social development. Now the most important question in the communities, especially in developing countries is how people can be entrepreneurs and how can they create entrepreneurial opportunities. Nowadays because of technological development people can communicate with each other in many different w...
متن کاملذخیره در منابع من
با ذخیره ی این منبع در منابع من، دسترسی به آن را برای استفاده های بعدی آسان تر کنید
برای دانلود متن کامل این مقاله و بیش از 32 میلیون مقاله دیگر ابتدا ثبت نام کنید
ثبت ناماگر عضو سایت هستید لطفا وارد حساب کاربری خود شوید
ورودعنوان ژورنال:
- CoRR
دوره abs/1308.0777 شماره
صفحات -
تاریخ انتشار 2013