RESEARCH PAPER ON CORRELATION

MaxQDPro TeamAnjan.K Harish.R

II Sem M.Tech CSE

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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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