research-article

User interactions in social networks and their implications

Authors:

Christo Wilson,

Bryce Boe,

Alessandra Sala,

Krishna P.N. Puttaswamy,

Ben Y. ZhaoAuthors Info & Claims

EuroSys '09: Proceedings of the 4th ACM European conference on Computer systems

Pages 205 - 218

https://doi.org/10.1145/1519065.1519089

Published: 01 April 2009 Publication History

Get Access

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

[1]

Lada A. Adamic, Orkut Buyukkokten, and Eytan Adar. A social network caught in the web. First Monday, 8(6), 2003.

Google Scholar

[2]

Yong-Yeol Ahn, S. Han, H. Kwak, S. Moon, and H. Jeong. Analysis of topological characteristics of huge online social networking services. In Proc. of the International World Wide Web Conference, 2007.

Digital Library

Google Scholar

[3]

L.A. N. Amaral, A. Scala,M. Barthelemy, and H. E. Stanley. Classes of small--world networks. In Proc. of National Academy of Sciences, pages 11149--11152, 2000.

Google Scholar

[4]

Albert-Laszlo Barabasi and Reka Albert. Emergence of scaling in random networks. Science, 286, 1999.

Google Scholar

[5]

Bryce Boe and Christo Wilson. crawl-e: Highly distributed web crawling framework written in python. Google Code, 2008.

Google Scholar

[6]

Andrei Broder, Ravi Kumar, Farzin Maghoul, Prabhakar Raghavan, Sridhar Rajagopalan, Raymie Stata, Andrew Tomkins, and Janet Wiener. Graph structure in the web: Experiments and models. In Proc. of the International World Wide Web Conference, 2000.

Digital Library

Google Scholar

[7]

H. Chun, H. Kwak, Y.H. Eom, Y.Y. Ahn, S. Moon, and H. Jeong. Comparison of online social relations in volume vs interaction: a case study of cyworld. In Proc. of Internet Measurement Conference, 2008.

Digital Library

Google Scholar

[8]

Aaron Clauset, Cosma Rohilla Shalizi, and M. E. J. Newman. Power-law distributions in empirical data. SIAM Review, to appear, 2009. See: <a href="http://arxiv.org/abs/0706.1062">arxiv:0706.1062</a>.

Digital Library

Google Scholar

[9]

J. Donath and D. Boyd. Public displays of connection. BT Technology Journal, 22(4), 2004.

Digital Library

Google Scholar

[10]

John R. Douceur. The Sybil attack. In Proc. of IPTPS, 2002.

Digital Library

Google Scholar

[11]

Facebook. Statistics. facebook.com, March 2008.

Google Scholar

[12]

S. Garriss, M. Kaminsky, M. J. Freedman, B. Karp, D. Mazieres, and H. Yu. Re: Reliable email. In Proc. of NSDI, San Jose, CA, May 2006.

Digital Library

Google Scholar

[13]

Ravi Kumar, Jasmine Novak, and Andrew Tomkins. Structure and evolution of online social networks. In Proc. of KDD, pages 611--617, 2006.

Digital Library

Google Scholar

[14]

Jure Leskovec and Eric Horvitz. Planetary-scale views on a large instant-messaging network. In Proc. of the International World WideWeb Conference, Beijing, China, April 2008.

Digital Library

Google Scholar

[15]

Sergio Marti, Prasanna Ganesan, and Hector Garcia-Molina. DHT routing using social links. In Proc. of IPTPS, San Diego, CA, February 2004.

Google Scholar

[16]

Stanley Milgram. The small world problem. Psychology Today, 6:62--67, 1967.

Google Scholar

[17]

StanleyMilgram. The familiar stranger: an aspect of urban anonymity. Addison--Wesley, 1977.

Google Scholar

[18]

A. Mislove, K. P. Gummadi, and P. Druschel. Exploiting social networks for internet search. In Proc. of HotNets, Irvine, CA, November 2006.

Google Scholar

[19]

Alan Mislove, Hema Swetha Koppula, Krishna P. Gummadi, Peter Druschel, and Bobby Bhattacharjee. Growth of the Flickr social network. In Proc. of WOSN, Seattle, WA, August 2008.

Digital Library

Google Scholar

[20]

Alan Mislove, Massimiliano Marcon, Krishna P. Gummadi, Peter Druschel, and Bobby Bhattacharjee. Measurement and analysis of online social networks. In Proc. of Internet Measurement Conference, October 2007.

Digital Library

Google Scholar

[21]

M. E. J. Newman. Mixing patterns in networks. Physical Review E, 67, 2003.

Google Scholar

[22]

Mark Sweney. Facebook sees first dip in uk users. guardian.co.uk, February 2008.

Google Scholar

[23]

Duncan J. Watts and Steven Strogatz. Collective dynamics of 'small-world' networks. Nature, (393):440--442, 1998.

Google Scholar

[24]

Ben Worthen. Bill Gates quits facebook. Wall Street Journal Online, Feb. 2008.

Google Scholar

[25]

Haifeng Yu, Phillip B. Gibbons, Michael Kaminsky, and Feng Xiao. Sybillimit: A near-optimal social network defense against sybil attacks. In Proc. of IEEE Security & Privacy, 2008.

Digital Library

Google Scholar

[26]

Haifeng Yu, Michael Kaminsky, Phillip B. Gibbons, and Abraham Flaxman. Sybilguard: defending against sybil attacks via social networks. In Proc. of SIGCOMM, 2006.

Digital Library

Google Scholar

Cited By

View all

Azogagh SBirba ZGambs SKillijian MVilela JSchulmann HLi N(2024)Crypto'Graph: Leveraging Privacy-Preserving Distributed Link Prediction for Robust Graph LearningProceedings of the Fourteenth ACM Conference on Data and Application Security and Privacy10.1145/3626232.3653257(199-210)Online publication date: 19-Jun-2024
https://dl.acm.org/doi/10.1145/3626232.3653257
Amin MLiu LChua TNgo CKumar RLauw HKa-Wei Lee R(2024)Understanding the Impact of COVID-19 on Online Eating Disorder Communities on RedditCompanion Proceedings of the ACM Web Conference 202410.1145/3589335.3652506(1695-1704)Online publication date: 13-May-2024
https://dl.acm.org/doi/10.1145/3589335.3652506
Yu WKong JHao FLi JLiu Y(2024)Formal Modeling and Analysis of User Activity Sequence in Online Social Networks: A Stochastic Petri Net-Based ApproachIEEE Transactions on Computational Social Systems10.1109/TCSS.2023.333593511:3(3580-3593)Online publication date: Jun-2024
https://doi.org/10.1109/TCSS.2023.3335935
Show More Cited By

Index Terms

User interactions in social networks and their implications
1. Software and its engineering
  1. Software organization and properties
    1. Software system structures
      1. Distributed systems organizing principles
        Organizing principles for web applications

Recommendations

Characterizing user behavior in online social networks
IMC '09: Proceedings of the 9th ACM SIGCOMM conference on Internet measurement

Understanding how users behave when they connect to social networking sites creates opportunities for better interface design, richer studies of social interactions, and improved design of content distribution systems. In this paper, we present a first ...
Characterizing user navigation and interactions in online social networks

Understanding how users navigate and interact when they connect to social networking sites creates opportunities for better interface design, richer studies of social interactions, and improved design of content distribution systems. In this paper, we ...
Social exchange in online social networks. The reciprocity phenomenon on Facebook

Our research is focused on reciprocity, which is crucial for social exchanges.The online social network platform of our choice was Facebook, which is one of the most successful online social sites.In our study we found strong empirical evidence that an ...

Reviews

Reviewer: Burkhard Englert

Recently, several applications have been designed that use social networking relationships "to improve [the] security and performance [of] applications such as email, Web browsing, and overlay routing." Such applications usually base trust on the existence of social network connections between participants. In this paper, Wilson et al. demonstrate the limitations of social network connections alone as the basis for trust, and make suggestions for a better approach. They show, based on a large social network and user interaction analysis on the Facebook network, that only a fraction of users' social network connections (friends) represent actual social interactions. Hence, they propose the use of interaction graphs instead of the customary social graphs as the basis of trust decisions in applications. "An interaction graph contains all nodes from its social graph counterpart, but only a subset of the links"-those over which real interactions occur. Social interaction graphs reduce the social graphs and therefore enhance their value. As the authors further demonstrate, this approach leads to improved performance in algorithms such as Reliable Email [1] and SybilGuard [2]. This well-written paper deserves credit for its formal verification of the seemingly intuitive observation that social network connections do not always represent social network interactions, and for its careful analysis of the relationship between social and interaction graphs. One of its main weaknesses is its exclusive focus on Facebook. As a result, the issue of how to construct interaction graphs for other social networking sites is never addressed. In the end, what remains is a valuable suggestion-to consider social interactions instead of social connections-to anyone who wants to develop applications based on social networks. Online Computing Reviews Service

Access critical reviews of Computing literature here

Become a reviewer for Computing Reviews.

Comments

Please enable JavaScript to view thecomments powered by Disqus.

Information & Contributors

Information

Published In

EuroSys '09: Proceedings of the 4th ACM European conference on Computer systems

April 2009

342 pages

ISBN:9781605584829

DOI:10.1145/1519065

General Chair:
Wolfgang Schröder-Preikschat
FAU Erlangen-Nuremberg, Germany
,
Program Chairs:
John Wilkes
Google, Inc., USA
,
Rebecca Isaacs
Microsoft Research, Cambridge, UK

Permission to make digital or hard copies of all or part of this work for personal or classroom use is granted without fee provided that copies are not made or distributed for profit or commercial advantage and that copies bear this notice and the full citation on the first page. Copyrights for components of this work owned by others than ACM must be honored. Abstracting with credit is permitted. To copy otherwise, or republish, to post on servers or to redistribute to lists, requires prior specific permission and/or a fee. Request permissions from [email protected]

Publisher

Association for Computing Machinery

New York, NY, United States

Publication History

Published: 01 April 2009

Permissions

Request permissions for this article.

Request Permissions

Check for updates

Author Tag

online social networks

Qualifiers

Research-article

Conference

EuroSys '09

Sponsor:

SIGOPS

EuroSys '09: Fourth EuroSys Conference 2009

April 1 - 3, 2009

Nuremberg, Germany

Acceptance Rates

Overall Acceptance Rate 241 of 1,308 submissions, 18%

Upcoming Conference

EuroSys '25

Sponsor:
sigops

Twentieth European Conference on Computer Systems

March 30 - April 3, 2025

Rotterdam , Netherlands

Contributors

Other Metrics

View Article Metrics

Bibliometrics & Citations

Bibliometrics

Article Metrics

587
Total Citations
View Citations
6,672
Total Downloads

Downloads (Last 12 months)203
Downloads (Last 6 weeks)15

Reflects downloads up to 30 Nov 2024

Other Metrics

View Author Metrics

Citations

Cited By

View all

Azogagh SBirba ZGambs SKillijian MVilela JSchulmann HLi N(2024)Crypto'Graph: Leveraging Privacy-Preserving Distributed Link Prediction for Robust Graph LearningProceedings of the Fourteenth ACM Conference on Data and Application Security and Privacy10.1145/3626232.3653257(199-210)Online publication date: 19-Jun-2024
https://dl.acm.org/doi/10.1145/3626232.3653257
Amin MLiu LChua TNgo CKumar RLauw HKa-Wei Lee R(2024)Understanding the Impact of COVID-19 on Online Eating Disorder Communities on RedditCompanion Proceedings of the ACM Web Conference 202410.1145/3589335.3652506(1695-1704)Online publication date: 13-May-2024
https://dl.acm.org/doi/10.1145/3589335.3652506
Yu WKong JHao FLi JLiu Y(2024)Formal Modeling and Analysis of User Activity Sequence in Online Social Networks: A Stochastic Petri Net-Based ApproachIEEE Transactions on Computational Social Systems10.1109/TCSS.2023.333593511:3(3580-3593)Online publication date: Jun-2024
https://doi.org/10.1109/TCSS.2023.3335935
Uddin SLu HRahman AGao J(2024)A novel approach for assessing fairness in deployed machine learning algorithmsScientific Reports10.1038/s41598-024-68651-w14:1Online publication date: 1-Aug-2024
https://doi.org/10.1038/s41598-024-68651-w
Kavargyris DGeorgiou KAngelis L(2024)EchoSense: a framework for analyzing the echo chambers phenomenon: a case study on Qatar eventsSocial Network Analysis and Mining10.1007/s13278-024-01275-014:1Online publication date: 10-Jun-2024
https://doi.org/10.1007/s13278-024-01275-0
Kanavos AKaramitsos IMohasseb A(2023)Exploring Clustering Techniques for Analyzing User Engagement Patterns in Twitter DataComputers10.3390/computers1206012412:6(124)Online publication date: 19-Jun-2023
https://doi.org/10.3390/computers12060124
Balduf LKorczyński MAscigil OKeizer NPavlou GScheuermann BKról MMontpetit MLeivadeas AUhlig SJaved M(2023)The Cloud Strikes Back: Investigating the Decentralization of IPFSProceedings of the 2023 ACM on Internet Measurement Conference10.1145/3618257.3624797(391-405)Online publication date: 24-Oct-2023
https://dl.acm.org/doi/10.1145/3618257.3624797
Nakajima KShudo K(2023)Random Walk Sampling in Social Networks Involving Private NodesACM Transactions on Knowledge Discovery from Data10.1145/356138817:4(1-28)Online publication date: 24-Feb-2023
https://dl.acm.org/doi/10.1145/3561388
Tang JQin TKong DZhou ZLi XWu YGu J(2023)Anomaly Detection in Social-Aware IoT NetworksIEEE Transactions on Network and Service Management10.1109/TNSM.2023.324232020:3(3162-3176)Online publication date: Sep-2023
https://doi.org/10.1109/TNSM.2023.3242320
Gong QLiu YZhang JChen YLi QXiao YWang XHui P(2023)Detecting Malicious Accounts in Online Developer Communities Using Deep LearningIEEE Transactions on Knowledge and Data Engineering10.1109/TKDE.2023.323783835:10(10633-10649)Online publication date: 1-Oct-2023
https://doi.org/10.1109/TKDE.2023.3237838
Show More Cited By

View Options

Login options

Check if you have access through your login credentials or your institution to get full access on this article.

Cited By

Index Terms

Recommendations

Characterizing user behavior in online social networks

Characterizing user navigation and interactions in online social networks

Social exchange in online social networks. The reciprocity phenomenon on Facebook

Reviews

Access critical reviews of Computing literature here