Presentation on theme: "1 Diffusion in (Social) networks Rajesh Sharma October, 2014 This presentation is based on several works,"— Presentation transcript:
1 Diffusion in (Social) networks Rajesh Sharma http://rajshpec.github.io/ firstname.lastname@example.org October, 2014 This presentation is based on several works, including some with: Prof. Danilo Montessi (University of Bologna, Italy), Prof. Matteo Magnani (Uppsala University, Sweden) Prof. Anwitaman Datta (NTU, Singapore), Prof. Mostafa Salehi (University of Tehran, Iran) *Some slides’ content from Jure Leskovec ‘s course work. Jure Leskovec
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.
Networks: collection of objects where some pairs of objects are connected by links Protein-protein ISP: Router etc Transportation: Metro Sexual contact Co-citationRecipeFriendship Human Diseases Food Web
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.
Networks and Agents   Network Science, Barabassi 
Affect of Diffusion in ML Networks Internal Entity Diffusion process happening in a network affecting internal entities. Example: – Influence (product, behavior etc) External Entity A diffusion process happening in a network affecting external entity Example: – Effect of tweets on stock prices
Diffusion Dynamics: What can be done? B) Explanatory/Empirical Analysis Infer the underlying spreading cascade. Questions – How Diffusion look like – Cascades look like ? C) Algorithms – Influence maximization – Outbreak detection – etc 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?
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  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 Relv Nbrs – Forward to all relevant nbrs Duplication saving : social triad a & b don’t send each other Not for cases like c What about non-relv Nbrs – 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 13 0 a b c d p e e i i j j k k l l n m h α, β, γ can be change Feedback mechanism
More on Information Dissemination Swarm Particle Approach  Communities: Multi-Dimensional Network (based on relations) Particle swarm technique - Mobility (particles/agent can move), Orthogonal to GoDisco ( as multi-dim and mobility). GoDisco++  – Took best out of ICDCN 2011 and 2012 approaches. – Social sciences plus multi-dimensional network. 15.  Rajesh Sharma and Anwitaman Datta, Decentralized information dissemination in multidimensional semantic social overlays, ICDCN 2012, Hongkong.  Rajesh Sharma and Anwitaman Datta. GoDisco++: A Gossip algorithm for information dissemination in multi-dimensional community networks. Journal of Pervasive and Mobile Computing, Oct, 2012
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
Modeling: cascade process C1: (v 4,l 2 ) C2 : (v 4,l 1 ) Diffusion network: Aggregation of cascades C1 and C2  Spreading processes in Multilayer Networks, Mostafa Salehi, Rajesh Sharma, Moreno Marzolla, Danilo Montesi, Payam Siyari, and Matteo Magnani, under review at IEEE Transactions on Network Sceience & Engg.
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 – ??
Dependent variables used in different diffusion studies
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
History of Diffusion (Time Line) 196719781993 Milgram Navigation in small world  Granoveter: Threshold Model Internet 2001 Wiki, Friendster, Myspace, FB, Blogs, Flickr, Youtube, smartphones. SW: Small World Vesigpinani: underlying n/w is important 2015 AIDS impact on Swedish population. 1975 Epidemic model  2014 SF: Scale Free 1998 ?? 1999
Milgram Reloaded! Attempt to understand the navigation process Multiple networks (FB, Twitter, WhatsApp etc) Across the Globe Multiple originators Multiple targets Multi Lingual T1T1 O1O1 O2O2 O3O3 O4O4 O5O5 T2T2 T4T4 T3T3 T5T5 T6T6 Output: Average path length, Network usage (geographically), orig target impact
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/http://m.web.cs.unibo.it/ If you have comments or feedback. Please contact: – email@example.com or firstname.lastname@example.org email@example.com@gmail.com
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
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?
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)
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
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
Rajesh Sharma University of Bologna http://rajshpec.github.io/ firstname.lastname@example.org Research Group: http://sigsna.net/impact/Research Group: http://sigsna.net/impact/ Thank you !! Questions?