AWeighted Parsimony Model for Community Detection in Complex Networks∗

نویسندگان

  • Junhua Zhang
  • Xiang-Sun Zhang
چکیده

Many real-world networks have a common feature of organization, i.e., community structure. Detecting this structure is fundamental for uncovering the links between the structure and the function in complex networks and for practical applications in many disciplines such as biology and sociology. In this paper we propose a weighted parsimony criterion for community detection in complex networks. This criterion relates communities with cliques (or complete subgraphs). Parsimony here means that as minimal as possible number of inserted and deleted edges is needed when we make the network considered become a disjoint union of cliques. A weight based on the topological features of the network is introduced to ensure the obtained subgraphs to be communities by balancing the inserted and deleted edges. Tests on real networks give excellent results.

برای دانلود متن کامل این مقاله و بیش از 32 میلیون مقاله دیگر ابتدا ثبت نام کنید

ثبت نام

اگر عضو سایت هستید لطفا وارد حساب کاربری خود شوید

منابع مشابه

An Optimized Firefly Algorithm based on Cellular Learning Automata for Community Detection in Social Networks

The structure of the community is one of the important features of social networks. A community is a sub graph which nodes have a lot of connections to nodes of inside the community and have very few connections to nodes of outside the community. The objective of community detection is to separate groups or communities that are linked more closely. In fact, community detection is the clustering...

متن کامل

Community Detection using a New Node Scoring and Synchronous Label Updating of Boundary Nodes in Social Networks

Community structure is vital to discover the important structures and potential property of complex networks. In recent years, the increasing quality of local community detection approaches has become a hot spot in the study of complex network due to the advantages of linear time complexity and applicable for large-scale networks. However, there are many shortcomings in these methods such as in...

متن کامل

A 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 کامل

Detecting Overlapping Communities in Social Networks using Deep Learning

In network analysis, a community is typically considered of as a group of nodes with a great density of edges among themselves and a low density of edges relative to other network parts. Detecting a community structure is important in any network analysis task, especially for revealing patterns between specified nodes. There is a variety of approaches presented in the literature for overlapping...

متن کامل

Detecting community structure: From parsimony to weighted parsimony

Community detection has attracted a great deal of attention in recent years. A parsimony criterion for detecting this structure means that as minimal as possible number of inserted and deleted edges is needed when we make the network considered become a disjoint union of cliques. However, many small groups of nodes are obtained by directly using this criterion to some networks especially for sp...

متن کامل

ذخیره 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 نام

اگر عضو سایت هستید لطفا وارد حساب کاربری خود شوید

عنوان ژورنال:

دوره   شماره 

صفحات  -

تاریخ انتشار 2009