Your friends have more friends than you do: identifying influential mobile users through random walks

In this paper, we study the problem of identifying influential users in mobile social networks. Traditional approaches find these users through centralized algorithms on either friendship or social-contact graphs of all users. However, the computational complexity of these algorithms is known to be very high, making them unsuitable for large-scale networks. We propose a lightweight and distributed protocol, iWander, to identify influential users through fixed-length random walks. To the best of our knowledge, we are the first to design a distributed protocol on smartphones that leverages random walks for identifying influential mobile users, although this technique has been used in other areas. The most attractive feature of iWander is its extremely low message overhead, which lends itself well to mobile applications. We evaluate the performance of iWander for two applications, targeted immunization of infectious diseases and target-set selection for information dissemination. Through extensive simulation studies using a real-world mobility trace, we demonstrate that targeted immunization using iWander achieves a comparable performance with a degree-based immunization policy that vaccinates users with large number of contacts first, while consuming only less than 1% of this policy's message overhead. We also show that target-set selection based on iWander outperforms the random and degree-based target-set selections for information dissemination in several scenarios.

[1]  Aravind Srinivasan,et al.  Modelling disease outbreaks in realistic urban social networks , 2004, Nature.

[2]  Walter Willinger,et al.  On Unbiased Sampling for Unstructured Peer-to-Peer Networks , 2006, IEEE/ACM Transactions on Networking.

[3]  Minas Gjoka,et al.  Walking in Facebook: A Case Study of Unbiased Sampling of OSNs , 2010, 2010 Proceedings IEEE INFOCOM.

[4]  Deborah Estrin,et al.  Rumor Routing Algorithm For Sensor Networks , 2002 .

[5]  Maria A. Kazandjieva,et al.  A high-resolution human contact network for infectious disease transmission , 2010, Proceedings of the National Academy of Sciences.

[6]  Nam P. Nguyen,et al.  Overlapping communities in dynamic networks: their detection and mobile applications , 2011, MobiCom.

[7]  Edsger W. Dijkstra,et al.  Termination Detection for Diffusing Computations , 1980, Inf. Process. Lett..

[8]  Lee Humphreys,et al.  It's Time to Eat! Using Mobile Games to Promote Healthy Eating , 2010, IEEE Pervasive Computing.

[9]  Wei Chen,et al.  Efficient influence maximization in social networks , 2009, KDD.

[10]  Aravind Srinivasan,et al.  eDiscovery: Energy efficient device discovery for mobile opportunistic communications , 2012, 2012 20th IEEE International Conference on Network Protocols (ICNP).

[11]  Zygmunt J. Haas,et al.  The shared wireless infostation model: a new ad hoc networking paradigm (or where there is a whale, there is a way) , 2003, MobiHoc '03.

[12]  Heiko Rieger,et al.  Random walks on complex networks. , 2004, Physical review letters.

[13]  Matthew Richardson,et al.  Mining the network value of customers , 2001, KDD '01.

[14]  Timur Friedman,et al.  Fixed point opportunistic routing in delay tolerant networks , 2008, IEEE Journal on Selected Areas in Communications.

[15]  Deborah Estrin,et al.  Rumor routing algorthim for sensor networks , 2002, WSNA '02.

[16]  Henning Schulzrinne,et al.  Effects of power conservation, wireless coverage and cooperation on data dissemination among mobile devices , 2001, MobiHoc '01.

[17]  David Lazer,et al.  Inferring friendship network structure by using mobile phone data , 2009, Proceedings of the National Academy of Sciences.

[18]  Cecilia Mascolo,et al.  SociableSense: exploring the trade-offs of adaptive sampling and computation offloading for social sensing , 2011, MobiCom.

[19]  Aravind Srinivasan,et al.  Mobile Data Offloading through Opportunistic Communications and Social Participation , 2012, IEEE Transactions on Mobile Computing.

[20]  Matthew Richardson,et al.  Mining knowledge-sharing sites for viral marketing , 2002, KDD.

[21]  Liam McNamara,et al.  Media sharing based on colocation prediction in urban transport , 2008, MobiCom '08.

[22]  David A. Maltz,et al.  Worm origin identification using random moonwalks , 2005, 2005 IEEE Symposium on Security and Privacy (S&P'05).

[23]  Ciro Cattuto,et al.  High-Resolution Measurements of Face-to-Face Contact Patterns in a Primary School , 2011, PloS one.

[24]  W. Marsden I and J , 2012 .

[25]  R. Christley,et al.  Infection in social networks: using network analysis to identify high-risk individuals. , 2005, American journal of epidemiology.

[26]  Mingyan Liu,et al.  Building realistic mobility models from coarse-grained traces , 2006, MobiSys '06.

[27]  Christophe Diot,et al.  Impact of Human Mobility on Opportunistic Forwarding Algorithms , 2007, IEEE Transactions on Mobile Computing.

[28]  Rayleigh The Problem of the Random Walk , 1905, Nature.

[29]  Donald F. Towsley,et al.  Estimating and sampling graphs with multidimensional random walks , 2010, IMC '10.

[30]  Cecilia Mascolo,et al.  Selective Reprogramming of Mobile Sensor Networks through Social Community Detection , 2010, EWSN.

[31]  Marc Lelarge,et al.  Efficient control of epidemics over random networks , 2009, SIGMETRICS '09.

[32]  Michael Kaminsky,et al.  SybilGuard: defending against sybil attacks via social networks , 2006, SIGCOMM.

[33]  W. O. Kermack,et al.  A contribution to the mathematical theory of epidemics , 1927 .

[34]  Mark E. J. Newman A measure of betweenness centrality based on random walks , 2005, Soc. Networks.

[35]  Reuven Cohen,et al.  Efficient immunization strategies for computer networks and populations. , 2002, Physical review letters.

[36]  Matthew Mohebbi,et al.  Assessing Google Flu Trends Performance in the United States during the 2009 Influenza Virus A (H1N1) Pandemic , 2011, PloS one.

[37]  Stratis Ioannidis,et al.  Dissemination in opportunistic mobile ad-hoc networks: The power of the crowd , 2011, 2011 Proceedings IEEE INFOCOM.

[38]  N. Christakis,et al.  Social Network Sensors for Early Detection of Contagious Outbreaks , 2010, PloS one.

[39]  László Lovász,et al.  Random Walks on Graphs: A Survey , 1993 .

[40]  Romit Roy Choudhury,et al.  Micro-Blog: sharing and querying content through mobile phones and social participation , 2008, MobiSys '08.

[41]  S. Feld Why Your Friends Have More Friends Than You Do , 1991, American Journal of Sociology.

[42]  David Tse,et al.  Mobility increases the capacity of ad-hoc wireless networks , 2001, Proceedings IEEE INFOCOM 2001. Conference on Computer Communications. Twentieth Annual Joint Conference of the IEEE Computer and Communications Society (Cat. No.01CH37213).

[43]  A. Barabasi,et al.  Halting viruses in scale-free networks. , 2001, Physical review. E, Statistical, nonlinear, and soft matter physics.

[44]  Vikram Srinivasan,et al.  PeopleNet: engineering a wireless virtual social network , 2005, MobiCom '05.

[45]  Eli Upfal,et al.  Probability and Computing: Randomized Algorithms and Probabilistic Analysis , 2005 .