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Diffusion in (Social) networks

Rajesh Sharmahttp://rajshpec.github.io/rajesh.sharma@unibo.it

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.

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 etcTransportation: 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 & DiffusionNetworks

DiffusionHuman-Human

Network

Comm. NetworkEg: OSN,

Internet, Mobile

Innovation

Virus

Rumor

Behavior

Idea, Innovation

Idea, Innovation

SARS, Virus

Transportation Network

Goods

Vegetables etc

Occupy Square

Smoking, Selfe

Selfe

Maria,Ronaldo

Inflation

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?

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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

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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]

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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

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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)

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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

0

ab

c

d

p e

i j

k

l

n

m

h

• α, β, γ can be change• Feedback mechanism

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Message Dissemination

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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 Anwitaman Datta , Decentralized information dissemination in multidimensional semantic social overlays, ICDCN 2012, Hongkong.

[4] 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: (v4,l2)

• C2 : (v4,l1)

• Diffusion network: Aggregation of cascades C1 and C2[5] 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)1967 1978 1993

MilgramNavigation in small world [1]

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 [2]

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

T1

O1

O2

O3

O4 O5

T2T4

T3

T5 T6

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/• If you have comments or feedback. Please contact:– rajesh.sharma@unibo.it or rajshpec@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 SharmaUniversity of Bologna

http://rajshpec.github.io/rajesh.sharma@unibo.it

Research Group: htt p://sigsna.net/impact/

Thank you !!Questions?