Diffusion in (Social) networks

1. Diffusion in (Social) networks Rajesh Sharma http://rajshpec.github.io/ rajesh.sharma@unibo.it October, 2014 This presentation is based on several works, including some with:Prof. DaniloMontessi (University of Bologna, Italy), Prof. MatteoMagnani (Uppsala University, Sweden) Prof. AnwitamanDatta (NTU, Singapore), Prof. MostafaSalehi (University of Tehran, Iran) *Some slides’ content from Jure Leskovec ‘s course work.

2. Agenda • Preliminary • Overview of Networks • Diffusion on Networks in Monoplex • Models, Algorithms etc. • Algorithm for diffusion in decentralized settings. • Diffusion on Networks in Multilayer Networks. • Models, Algorithms etc. • Conclusion & Future work.

3. Networks: collection of objects where some pairs of objects are connected by links Protein-protein Transportation: Metro ISP: Router etc Food Web Human Diseases Sexual contact Friendship Recipe Co-citation

4. Network Really Matters • If you want to understand the structure of the Web, it is hopeless without working with the Web’s topology. • If you want to understand the spread of diseases, can you do it without social networks? • If you want to understand dissemination of news or evolution of science, it is hopeless without considering the information networks.

5. Networks & Diffusion Networks Diffusion Human-Human Network Innovation Idea, Innovation SARS, Virus Goods Inflation Smoking, Selfe Transportation Network Vegetables etc Virus Idea, Innovation Maria,Ronaldo Rumor Comm. Network Eg: OSN, Internet, Mobile Occupy Square Behavior Selfe

6. Affect of Diffusion in ML Networks Internal Entity External Entity A diffusion process happening in a network affecting external entity Example: Effect of tweets on stock prices • Diffusion process happening in a network affecting internal entities. • Example: • Influence (product, behavior etc)

7. Diffusion Dynamics: What can be done? A)Models: • Decision Based Models • Independent Contagion Model • Threshold Model • Questions: • Finding Influential Nodes • Detecting cascades • Epidemic Based Models • SIS: Susceptible-Infected-Susceptible (e.g., Flu) • SIR : Susceptible Infected Recover (e.g., chicken pox) • Question: • Virus will take over the network? B) Explanatory/Empirical Analysis • Infer the underlying spreading cascade. • Questions • How Diffusion look like • Cascades look like ? C) Algorithms • Influence maximization • Outbreak detection • etc

8. Information Dissemination: Algorithm • Objectives • Effective • High precision (low spam) & recall (good coverage) • Efficient • Low latency, low duplication • Challenges : Decentralized settings • No global list, no explicit subscriptions or coordination • Intuition • Use social links in each hop • Locally available (interest) information • Less likely to be spammed • Easier accountability

9. Approach/Algorithm • Two logically independent mechanisms/phases • Control phase (runs in the background) • collect neighbor nodes’ information (interest, degree) • dissemination behavior (forwarding behavior, activeness) • Propagation of messages using selective gossip [4] Anwitaman Datta and Rajesh Sharma,GoDisco: Selective Gossip based Dissemination of Information in Social Community based Overlays,ICDCN 2011 [ best paper award in Networking track]

10. Intuitions for designing selective gossip • Social science principals • Reciprocity based incentives • Social triads to reduce duplicates • Feedback • Learning & adapting to neighbor interests • Interest communities • Naturally clustered • But there may be isolated islands

11. Information agent (IA) categories • Interest Classification : • main Category (MC) • subcategory (SC) • Order of preference • shared main category • irrelevant but good forwarding history • irrelevant but well connected (high degree)

12. Approach • If any RelvNbrs • Forward to all relevant nbrs • Duplication saving : social triad • a & b don’t send each other • Not for cases like c • What about non-relvNbrs • Send to e (closely related) • With probability p • Boundary nodes • αh + βd + γa (h – history, d - degree, a-activeness ) • C selects j • j starts a Random Walk d 0 h p e b a c m i j l k n • α, β, γ can be change • Feedback mechanism

13. Message Dissemination

14. More on Information Dissemination • Swarm Particle Approach [2] • Communities: Multi-Dimensional Network (based on relations) • Particle swarm technique - Mobility (particles/agent can move), • Orthogonal to GoDisco ( as multi-dim and mobility). • GoDisco++ [3] • Took best out of ICDCN 2011 and 2012 approaches. • Social sciences plus multi-dimensional network. . • [3] Rajesh Sharma and AnwitamanDatta ,Decentralized information dissemination in multidimensional semantic social overlays,ICDCN 2012, Hongkong. • [4] Rajesh Sharma and AnwitamanDatta. GoDisco++: A Gossip algorithm for information dissemination in multi-dimensional community networks. Journal of Pervasive and Mobile Computing, Oct, 2012

15. Multilayer Networks • Multiplex networks • Every node is present in every network. • multiple types of Relationships. • Interconnected networks • Not every node is present in every network. • Multiple networks. • Model • Diffusion

16. Modeling: cascade process • C1: (v4,l2) • C2 : (v4,l1) • Diffusion network: Aggregation of cascades C1 and C2 • [5] Spreading processes in Multilayer Networks, MostafaSalehi, Rajesh Sharma, Moreno Marzolla, DaniloMontesi, Payam • Siyari, and MatteoMagnani, under review at IEEE Transactions on Network Sceience & Engg.

17. 4 possibilities of diffusion in ML • Same-node inter-layer • Cascade switches layer but remains on the same node • Facebook post is shared on Twitter • Other-node inter-layer • Cascade continues spreading to another node in another layer • The spread of a disease in an interconnected network of cities • Other-node intra-layer • Cascade continues spreading through the same layer. • Retweeting a post in Twitter • Same-node intra-layer • ??

18. Dependent variables used in different diffusion studies

19. Milgram Experiment. (late 1960s) • The navigation problem • Small world community. • The experiment set up • One target (Massachusetts) • Many originators. (Nebraska) • Acquaintance chains of Letters • Output • Six degrees of Separation • New version (2003) by Dodds et al. • Multiple source and Targets • Web based experiment

20. History of Diffusion (Time Line) 2014 2015 1967 1975 1978 1993 1998 1999 2001 ?? Internet Milgram Navigation in small world [1] Granoveter: Threshold Model Epidemic model [2] AIDS impact on Swedish population. SF: Scale Free Wiki, Friendster, Myspace, FB, Blogs, Flickr, Youtube, smartphones. SW: Small World Vesigpinani: underlying n/w is important

21. Milgram Reloaded! • Attempt to understand the navigation process • Multiple networks (FB, Twitter, WhatsApp etc) • Across the Globe • Multiple originators • Multiple targets • Multi Lingual Output: Average path length, Network usage (geographically), orig< -- >target impact T2 T1 T4 O2 O4 O5 O1 T5 T6 O3 T3

22. Milgram Reloaded! • What data we will ask* • Who are you : Email ID or Phone No • Network: Through what network you received it. • Who sent you: ID of the person • Which networks are you going to use to move the message towards its destination ? • Web Link: http://m.web.cs.unibo.it/ • If you have comments or feedback. Please contact: • rajesh.sharma@unibo.it or rajshpec@gmail.com

23. Reasoning about Networks • How do we reason about networks? • Empirical: Study network data to find organizational principles • How do we measure and quantify networks? • Mathematical models: Graph theory and statistical models • Models allow us to understand behaviors and distinguish surprising from expected phenomena. • Algorithms: for analyzing graphs • Hard computational challenges

24. Networks: Structure & Process • What do we study in networks? • Structure and evolution: • What is the structure of a network? • Why and how did it come to have such structure? • Processes and dynamics: • Networks provide “skeleton for spreading of information, behavior, diseases • How do information and diseases spread?

25. Networks: Impact • Companies: Google (382.61B), Cisco (125.29B), Facebook (207.04B), Twitter (25.32B), LinkedIn (28.9B) • Predicting Epidemics : Flu • Intelligence and fighting (cyber) terrorism: Find the leaders/hubs of terrorist org/regimes • Financial Impact: Recession in Europe (who is lending whom)

26. Networks: Size Matters • Network data: Orders of magnitude • 436-node network of email exchange at a corporate • research lab [Adamic-Adar, SocNets ‘03] • 43,553-node network of email exchange at an • university [Kossinets-Watts, Science ‘06] • 4.4-million-node network of declared friendships on a • blogging community [Liben-Nowell et al., PNAS ‘05] • 240-million-node network of communication on • Microsoft Messenger [Leskovec-Horvitz, WWW ’08] • 800-million-node Facebook network [Backstrom et al. ‘1

27. Group Activity • Big data : Network (and non network) data (mostly from web). • Understand and analysis • Few Examples: • Impact of Tweets on : • Financial patterns. • Reputation of Companies • Community patterns in networks: Information dissemination. • GPS data : insurance fraud

28. Thank you!!Questions? Rajesh Sharma University of Bologna http://rajshpec.github.io/ rajesh.sharma@unibo.it Research Group: http://sigsna.net/impact/