User interactions in social networks and their implications

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User interactions in social networks and their implications

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European Conference on Computer Systems archive
Proceedings of the 4th ACM European conference on Computer systems table of contents

Nuremberg, Germany

SESSION: Clients and the web table of contents

Pages 205-218

Year of Publication: 2009

ISBN:978-1-60558-482-9

Authors

Christo Wilson	University of California at Santa Barbara, Santa Barbara, CA, USA
Bryce Boe	University of California at Santa Barbara, Santa Barbara, CA, USA
Alessandra Sala	University of California at Santa Barbara, Santa Barbara, CA, USA
Krishna P.N. Puttaswamy	University of California at Santa Barbara, Santa Barbara, CA, USA
Ben Y. Zhao	University of California at Santa Barbara, Santa Barbara, CA, USA

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ACM: Association for Computing Machinery

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ACM New York, NY, USA

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ABSTRACT

Social networks are popular platforms for interaction, communication and collaboration between friends. Researchers have recently proposed an emerging class of applications that leverage relationships from social networks to improve security and performance in applications such as email, web browsing and overlay routing. While these applications often cite social network connectivity statistics to support their designs, researchers in psychology and sociology have repeatedly cast doubt on the practice of inferring meaningful relationships from social network connections alone.

This leads to the question: Are social links valid indicators of real user interaction? If not, then how can we quantify these factors to form a more accurate model for evaluating socially-enhanced applications? In this paper, we address this question through a detailed study of user interactions in the Facebook social network. We propose the use of interaction graphs to impart meaning to online social links by quantifying user interactions. We analyze interaction graphs derived from Facebook user traces and show that they exhibit significantly lower levels of the "small-world" properties shown in their social graph counterparts. This means that these graphs have fewer "supernodes" with extremely high degree, and overall network diameter increases significantly as a result. To quantify the impact of our observations, we use both types of graphs to validate two well-known social-based applications (RE and SybilGuard). The results reveal new insights into both systems, and confirm our hypothesis that studies of social applications should use real indicators of user interactions in lieu of social graphs.

REFERENCES

Note: OCR errors may be found in this Reference List extracted from the full text article. ACM has opted to expose the complete List rather than only correct and linked references.

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