This document discusses the development of a community discovery framework called MultiComm, which aims to identify seed-based communities within multi-dimensional networks, particularly for data mining applications in social media. It evaluates interactions among users, tags, and other entities, proposing a method that calculates probabilities to generate communities based on affinity. Experimental results indicate that MultiComm outperforms existing community detection algorithms in terms of accuracy using both synthetic and real-world data sets.

1. GLOBALSOFT TECHNOLOGIES
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Multi Comm: Finding Community Structure in Multi-
Dimensional Networks
Abstract
The main aim of this paper is to develop a community discovery scheme in a multi-dimensional network
for data mining applications. In online social media, networked data consists of multiple
dimensions/entities such as users, tags, photos, comments, and stories. We are interested in finding a
group of users who interact significantly on these media entities. In a co-citation network, we are
interested in finding a group of authors who relate to other authors significantly on publication
information in titles, abstracts, and keywords as multiple dimensions/entities in the network. The main
contribution of this paper is to propose a framework (MultiComm)to identify a seed-based community
in a multi-dimensional network by evaluating the affinity between two items in the same type of entity
(same dimension)or different types of entities (different dimensions)from the network. Our idea is to
calculate the probabilities of visiting each item in each dimension, and compare their values to generate
communities from a set of seed items. In order to evaluate a high quality of generated communities by
the proposed algorithm, we develop and study a local modularity measure of a community in a multi -
dimensional network. Experiments based on synthetic and real -world data sets suggest that the
proposed framework is able to find a community effectively. Experimental results have also shown that
the performance of the proposed algorithm is better in accuracy than the other testing algorithms in
finding communities in multi-dimensional networks.
Existing system
The main aim of this paper is to develop a community discovery scheme in a multi-dimensional network
for data mining applications. In online social media, networked data consists of multiple
dimensions/entities such as users, tags, photos, comments, and stories. We are interested in finding a
group of users who interact significantly on these media entities. In a co-citation network, we are

2. interested in finding a group of authors who relate to other authors significantly on publication
information in titles, abstracts, and keywords as multiple dimensions/entities in the network.
Proposed system
The main contribution of this paper is to propose a framework (MultiComm)to identify a seed-based
community in a multi-dimensional network by evaluating the affinity between two items in the same
type of entity (same dimension)or different types of entities (different dimensions)from the network.
Our idea is to calculate the probabilities of visiting each item in each dimension, and compare their
values to generate communities from a set of seed items. In order to evaluate a high quality of
generated communities by the proposed algorithm, we develop and study a local modularity measure of
a community in a multi-dimensional network. Experiments based on synthetic and real-world data sets
suggest that the proposed framework is able to find a community effectively. Experimental results have
also shown that the performance of the proposed algorithm is better in accuracy than the other test ing
algorithms in finding communities in multi-dimensional networks.
SYSTEM CONFIGURATION:-
HARDWARE CONFIGURATION:-
 Processor - Pentium –IV
 Speed - 1.1 Ghz
 RAM - 256 MB(min)
 Hard Disk - 20 GB
 Key Board - Standard Windows Keyboard
 Mouse - Two or Three Button Mouse
 Monitor - SVGA
SOFTWARE CONFIGURATION:-
 Operating System : Windows XP
 Programming Language : JAVA
 Java Version : JDK 1.6 & above.