Download Fast and accurate mining the community structure: integrating center

Fast and accurate mining the community structure: integrating center
locating and membership optimization
ABSTRACT
Despite being challenging, mining or detecting network communities has broad applications,
because nodes in the same community generally possess mutual properties or relationships
relative to nodes interconnecting different communities. For example, members in a social circle
may have common interests, a group of closely linked proteins often work together for a given
functional realization, and tweets under the same topic always spread the similar opinion. There
is a lot of evidence that community structures are crucial to the understanding of functional
properties of complex networks and are also very helpful for developing intelligent services,
such as developing tools for precision marketing, identifying target points for drug discovery,
searching and mining online social networks for trend predictions.
EXISTING SYSTEM
Most of the existing techniques view the community mining problem as an optimization problem
based on a given quality function(e.g., modularity), however none of them are grounded with a
systematic theory to identify the central nodes in the network. Moreover, how to reconcile the
mining efficiency and the community quality still remains an open problem.
DIS ADVANTAGES

cannot identify the central nodes using the quality function optimization
PROPOSED SYSTEM
In this paper, we attempt to address the above challenges by introducing a novel algorithm. First,
a kernel function with a tunable influence factor is proposed to measure the leadership of each
node, those nodes with highest local leadership can be viewed as the candidate central nodes.
Further Details Contact: A Vinay 9030333433, 08772261612, 9014123891 #301, 303 & 304, 3rd Floor,
AVR Buildings, Opp to SV Music College, Balaji Colony, Tirupati - 515702 Email:
[email protected] | www.takeoffprojects.
Then, we use a discrete-time dynamical system to describe the dynamical assignment of
community membership; and formulate the serval conditions to guarantee the convergence of
each node’s dynamic trajectory, by which the hierarchical community structure of the network
can be revealed. The proposed dynamical system is independent of the quality function used, so
could also be applied in other community mining models. Our algorithm is highly efficient: the
computational complexity analysis shows that the execution time is nearly linearly dependent on
the number of nodes in sparse networks. We finally give demonstrative applications of the
algorithm to a set of synthetic benchmark networks and also real-world networks to verify the
algorithmic performance.
ADVANTAGES

It can identify the central nodes with highest local leadership in a multi-resolution
manner.

The proposed method overcomes the resolution limit problem and is able to recover the
real number of communities and a good community partition.
MODULES

kernel function

discrete-time dynamical system

SYSTEM REQUIREMENTS
H/W System Configuration:
Processor
- Pentium –III

RAM
- 256 MB (min)

Hard Disk
- 20 GB

Key Board
-
Standard Windows Keyboard

Mouse
-
Two or Three Button Mouse
Further Details Contact: A Vinay 9030333433, 08772261612, 9014123891 #301, 303 & 304, 3rd Floor,
AVR Buildings, Opp to SV Music College, Balaji Colony, Tirupati - 515702 Email:
[email protected] | www.takeoffprojects.

Monitor
- SVGA
S/W System Configuration:
Operating System
: Windows95/98/2000/XP

Application Server
: Tomcat5.0/6.X

Front End
: HTML, Jsp

Scripts
: JavaScript.

Server side Script
: Java Server Pages.

Database
: MySQL 5.0

Database Connectivity
: JDBC
Further Details Contact: A Vinay 9030333433, 08772261612, 9014123891 #301, 303 & 304, 3rd Floor,
AVR Buildings, Opp to SV Music College, Balaji Colony, Tirupati - 515702 Email:
[email protected] | www.takeoffprojects.