Graph clustering in industrial networks

Dynamic Networks from Hierarchical Bayesian Graph Clustering

Biological networks change dynamically as protein components are synthesized and degraded. Understanding the time-dependence and, in a multicellular organism, tissue-dependence of a network leads to insight beyond a view that collapses time-varying interactions into a single static map. Conventional algorithms are limited to analyzing evolving networks by reducing them to a series of unrelated ...

Finding Community Base on Web Graph Clustering

Search Pointers organize the main part of the application on the Internet. However, because of Information management hardware, high volume of data and word similarities in different fields the most answers to the user s’ questions aren`t correct. So the web graph clustering and cluster placement in corresponding answers helps user to achieve his or her intended results. Community (web communit...

Clustering in Wireless Multimedia Sensor Networks Using Spectral Graph Partitioning

Wireless multimedia sensor network (WMSN) consists of sensors that can monitor multimedia data from its surrounding, such as capturing image, video and audio. To transmit multimedia information, large energy is required which decreases the lifetime of the network. In this paper we present a clustering approach based on spectral graph partitioning (SGP) for WMSN that increases the lifetime of th...

Graph Based Clustering for Anomaly Detection in IP Networks

finding community base on web graph clustering

search pointers organize the main part of the application on the internet. however, because of information management hardware, high volume of data and word similarities in different fields the most answers to the user s’ questions aren`t correct. so the web graph clustering and cluster placement in corresponding answers helps user to achieve his or her intended results. community (web communit...