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RESEARCH PAPER ON CORRELATION
MaxQDPro TeamAnjan.K Harish.R
II Sem M.Tech CSE
04/12/23 Research Paper On Corelation
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Prerequisites Introduction ◦ Paper◦ Key Terminologies◦ Abstract◦ Contributions Characterizing the Social Network Surfing the paper Future Work Conclusion References
AGENDA
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Data Mining ConceptsKey ConceptsCo-relation analysisSocial Networks
Instant Messaging or any Chat application Social and Behavioral Sciences Demographics Fair Knowledge of issues in Cyber World Enough Moral and emotional maturity to
understand it.
PREREQUISITES
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A MACROSCOPIC FOCUS ON THE PAPER
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“YES, THERE IS CORRELATION FROM SOCIAL NETWORKS TO PERSONAL BEHAVIOR ON THE WEB”
INTRODUCTION – PAPER (1)0
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Authors – Parag Singla of University
of Washington, Seattle, USA
Matthew Richardson, Microsoft R&D, Redmond,USA
Paper was first presented at International World Wide web Conference Committee(IW3C2) .
Study and survey started from 2005 and later
Permission to use the paper to classroom and personal reference use only.
Key words Social and
Behavioral Sciences Social Networks Instant Messaging Demographics
ACM Transaction on Knowledge Discovery Process ,April 2008.
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INTRODUCTION-KEY TERMINOLOGIES (2) Behavioral Sciences
A study of determining the behavior of a person or people in society
Social Network Social Structure made of nodes that are tied by one or more
specific types of interdependency, such as values, visions, ideas, financial exchange, friendship, sexual relationship, kinship
Social network analysis views social relationship in terms of nodes and ties.
Demographics refers to selected population characteristics as used in
government, marketing or opinion research, or the demographic profiles used in such research.
Commonly-used demographics include race, age, income, disabilities, mobility (in terms of travel time to work or number of vehicles available), educational attainment, home ownership, employment status, and even location.
The idea that people with similar characteristics tend to be connected is called homophily.
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INTRODUCTION-ABSTRACT (3) Characterizing the relationship that exists between a
person’s social group and his/her personal behavior has been a long standing goal of social network analysts.
In this paper, we apply data mining techniques to study this relationship for a population of over 10 million people, by turning to online sources of data.
The analysis reveals that people who chat with each other (using instant messaging) are more likely to share interests (their Web searches are the same or topically similar).
The more time they spend talking, the stronger this relationship is. People who chat with each other are also more likely to share other personal characteristics, such as their age and location (and, they are likely to be of opposite gender).
Similar findings hold for people who do not necessarily talk to each other but do have a friend in common.
Our analysis is based on a well-defined mathematical formulation of the problem
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Given that two people are connected over the Internet, paper answer some of the question of eager curiosity Are they similar to each other? How does their connection affect their personal
behavior? How does their behavior vary based on the type of
connection? Its finds its application in detecting Cyber Crime
since there is lot of stress Cyber Confidentiality. Also helps parents to understand behavior of
their wards and to protect them against vulgarity.
04/12/23Research Paper On Corelation
INTRODUCTION – CONTRIBUTIONS (4)
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CHARACTERIZING THE SOCIAL NETWORK
Search Engines are personalized Intelligent Chat like recommending a friend
to join a network or use an application Analyze the relation between communication
and personal behavior, we need two sources of data: (1) Who communicates with whom, and
(2) The characteristics of each person in the communication network. Demographic parameters are person’s age,
gender and geographical location Correlation hold on the metrics used.
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SURFING THE PAPER
Correlation is made on the basis of the time duration spent by person in the chat conversation.
Classification is done by Bayesian Classifier Datasets assumed are
Social Network Data Personal Interests Data Joining Data
Experiments Computing the similarities Establishing the correlation Varying talk time. Condition on the Personal attribute Effects of the Indirect links
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RELATED WORK
McPherson etal. give an excellent review of work done on homophily in real-world networks.
Sproull and Patterson discuss how the participation in online communities might affect the every day lives and behavior of the people in the physical world.
Handcock and Raftery’s model for social networks incorporates assumptions about transitivity in link structure.
"Web homophily" can be used to advantage in, for example, finding communities of Web pages and the ranking of Web pages.
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Search similarity and IM talk time extend to the case of whether users click on advertisements as well.
To build a more predictive model for both. Experiment on multi-user chat sessions, see
proposed model of correlations is valid. Query behavior of the users through time, to
discover what types of queries tend to spread through the network, and what other queries do not.
Finally, examine whether the correlations discovered here are found in other domains, such as online gaming environments and social networking sites
FUTURE WORK
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Users who talk to each other in an IM environment are significantly more likely to share interests than a random pair of users.
Probabilistic model over users and their attributes and relations.
The similarity between users strengthens with the amount of time they spend talking to each other.
First experimental study of its kind and demonstrate significant promise for further research in this area, paving the way for many advances
CONCLUSION
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[1] A. Abbott, editor. The American Journal of Sociology, 2006.
[2] P. Bearman, J. Moody, and K. Stovel. Chains of affection:the structure of adolescent romantic and sexual networks.American Journal of Sociology, 110:44–91, 2004.
[3] A. Broder. A taxonomy of web search. In 25th Intl. SIGIR,pages 3–10, 2002.
[4] S. Chien and N. Immorlica. Semantic similarity betweensearch engine queries using temporal correlation. In 14th Intl. WWW, pages 2–11, 2005.
[5] P. Domingos and M. Pazzani. On the optimality of the simple Bayesian classifier under zero-one loss. Machine Learning, 29:103–130, 1997.
REFERENCES
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PROPAGATE GOOD THING TO OTHER
Duty as responsible engineers towards protection of Environment
“Ecological Footprint”Mother Earth
Please Visit http://
www.lightchannels.comGrow or Plant a tree and give it a
name they are living being as well.
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THANK YOU
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