Public Web Status Monitoring System
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-dimension-based 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.
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 relation-type 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:
Ø
Social
dimension suffer from scalable in heterogeneity.
Ø 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:
Ø
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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