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SCALABLE LEARNING OF COLLECTIVE BEHAVIOUR

Platform : DATA MINING

IEEE Projects Years : 2012

SCALABLE LEARNING OF COLLECTIVE BEHAVIOUR

Abstract:
This study of collective behavior is to understand how individuals
behave in a social networking environment. Oceans of data generated by
social media like Face book, Twitter, Flicker, and YouTube present
opportunities and challenges to study collective behavior on a large scale. In
this work, we aim to learn to predict collective behavior in social media. In
particular, given information about some individuals, how can we infer the
behavior of unobserved individuals in the same network? A social-dimensionbased
approach has been shown effective in addressing the heterogeneity of
connections presented in social media. However, the networks in social
media are normally of colossal size, involving hundreds of thousands of
actors. The scale of these networks entails scalable learning of models for
collective behavior prediction. To address the scalability issue, we propose an
edge-centric clustering scheme to extract sparse social dimensions. With
sparse social dimensions, the proposed approach can efficiently handle
networks of millions of actors while demonstrating a comparable prediction
performance to other non-scalable methods.
Architecture:
Algorithm:
1. Algorithm for Learning of Collective Behavior
Input: network data, labels of some nodes, number of social
dimensions;
Output: labels of unlabeled nodes.
1. Convert network into edge-centric view.
2. Perform edge clustering as in Figure 5.
3. Construct social dimensions based on edge partition node belongs to
one community as long as any of its neighboring edges is in that
community.
4. Apply regularization to social dimensions.
5. Construct classifier based on social dimensions of labeled nodes.
6. Use the classifier to predict labels of unlabeled ones based on their
social dimensions.
Existing System:
As existing approaches to extract social dimensions suffer from
scalability, it is imperative to address the scalability issue. Connections in
social media are not homogeneous. People can connect to their family,
colleagues, college classmates, or buddies met online. Some relations are
helpful in determining a targeted behavior while others are not. This relationtype
information, however, is often not readily available in social media. A
direct application of collective inference or label propagation would treat
connections in a social network as if they were homogeneous.
Disadvantages:
 Social dimension suffer from scalable in heterogeneity.
 This heterogeneity of connections limits the effectiveness.
Proposed System:
A recent framework based on social dimensions is shown to be
effective in addressing this heterogeneity. The framework suggests a novel
way of network classification: first, capture the latent affiliations of actors by
extracting social dimensions based on network connectivity, and next, apply
extant data mining techniques to classification based on the extracted
dimensions.
In the initial study, modularity maximization was employed to extract
social dimensions. The superiority of this framework over other
representative relational learning methods has been verified with social
media data in. The original framework, however, is not scalable to handle
networks of colossal sizes because the extracted social dimensions are rather
dense. In social media, a network of millions of actors is very common. With
a huge number of actors, extracted dense social dimensions cannot even be
held in memory, causing a serious computational problem.
Sparsifying social dimensions can be effective in eliminating the
scalability bottleneck. In this work, we propose an effective edge-centric
approach to extract sparse social dimensions. We prove that with our
proposed approach, sparsity of social dimensions is guaranteed.
Advantages:
 An incomparable advantage of our model is that it easily scales
to handle networks with millions of actors while the earlier
models fail. This scalable approach offers a viable solution to
effective learning of online collective behavior on a large scale.
Modules:
1. Social dimension extraction:
The latent social dimensions are extracted based on network
topology to capture the potential affiliations of actors. These extracted
social dimensions represent how each actor is involved in diverse
affiliations. These social dimensions can be treated as features of
actors for subsequent discriminative learning. Since a network is
converted into features, typical classifiers such as support vector
machine and logistic regression can be employed. Social dimensions
extracted according to soft clustering, such as modularity
maximization and probabilistic methods, are dense.
2. Discriminative learning:
The discriminative learning procedure will determine which
social dimension correlates with the targeted behavior and then assign
proper weights. A key observation is that actors of the same affiliation
tend to connect with each other. For instance, it is reasonable to
expect people of the same department to interact with each other
more frequently. A key observation is that actors of the same
affiliation tend to connect with each other. For instance, it is
reasonable to expect people of the same department to interact with
each other more frequently. Hence, to infer actors’ latent affiliations,
we need to find out a group of people who interact with each other
more frequently than at random.
3. Chart Generation for Group/Month:
Two data sets reported in are used to examine our proposed
model for collective behavior learning. The first data set is acquired
from user interest, the second from concerning behavior; we study
whether or not a user visits a group of interest. Then generates chart
the based on the user visit group in the month.
4. Chart Generation for User/Group:
Two data sets reported in are used to examine our proposed
model for collective behavior learning. The first data set is acquired
from user interest, the second from concerning behavior; we study
whether or not a user visits a group of interest. Then generates chart
the based on the user visit group in the month.
System Requirements:
Hardware Requirements:
Processor : Intel Duel Core.
Hard Disk : 60 GB.
Floppy Drive : 1.44 Mb.
Monitor : LCD Colour.
Mouse : Optical Mouse.
RAM : 512 Mb.
Software Requirements:
Operating system : Windows XP.
Coding Language : ASP.Net with C#
Data Base : SQL Server 2005


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