MSRC, Cambridge, UK Dec 7, 2007 The Network is the People or The People are the Network (les gens...

MSRC, Cambridge, UKDec 7, 2007

The Network is the Peopleor

The People are the Network(les gens sont le reseau)

[email protected]://www.cl.cam.ac.uk/

~jac22Computer Laboratory

University of Cambridge & Thomson Research Paris& LIP6


Social Networks and Mobility are only just starting to be

combined.• Two boring ways to do this are:

– offering social network access on mobile device (merely a matter of coping with reduced network capacity and screen real estate, and somewhat different interaction paradigms);

– making location services that are somewhat socially aware (what used to be called "contextual computing").

• Studies of human mobility patterns reveal that one can extract rich social structural information directly from contact (co-location) distributions.


This talk in outline• Thomson Labs: combine so-called traditional

(legacy) social network structures (friends of friends on facebook or linkedin etc) with new mobility information (contact of contact in bluetooth or wifi encounters).

• Two uses of the combined information: – expanding and subtly altering one's social net

(allowing for the somewhat different nature of static references in online communities, from face-to-face encounters in Real Life);

– using the social network (a priori, and learned, contacts and interests and tags) for efficient dissemination of information in wireless ad hoc communities.


Social Structures Vs Network Structures

•Community structures– Social communities, i.e. affiliations– Topologically cohesive groups or modules

•Centralities– Social hubs, celebrities and postmen– Betweenness, closeness, inference power, centrality

• 1. Look at Structure of Human Social Mobility experimentally


Experimental setup

• iMotes– ARM processor– Bluetooth radio– 64k flash memory

• Bluetooth Inquiries– 5 seconds every 2 minutes– Log contact tuples:

• {MAC address, start time, end time}


Experimental devices


So we did a lot of experiments• Mix of ours and other research groups:

– Infocom conferences 05 and 06 (miami, barcelona)– In Cambridge (with 1st & 2nd yr undergrds)– Hong Kong (with High School, and random [people on street)– Plus others’ data

• from crawdad dbase @dartmouth, ucsd, toronto• From MIT Reality Mining dataset

• Typical data set involves:– 54 iMotes distributed, Experiment duration: 3 days– 41 yielded useful data– 11 with battery or packaging problem, 2 not returned


What we measure

• For a given pairs of nodes:– contact times and inter-contact times.

Duration of the experiment

an inter-contact a contact time

t


What we measure (cont’d)

• Distribution per event. ≠ seen at a random instant in time.

• Plot log-log distributions.

• We aggregate the data of different pairs.(see the following slides).


Example: a typical pair

α

cutoff


Examples : Other pairs


K-clique Community Definition

• Union of k-cliques reachable through a series of adjacent k-cliques [Palla et al]

• Adjacent k-cliques share k-1 nodes

• Members in a community reachable through well-connected well subsets

• Examples– 2-clique (connected components)– 3-clique (overlapping triangles)

• Overlapping feature

• Known Percolation threshold


K-clique Communities in Infocom06 Dataset

Barcelona Group (Spanish)

Paris Group A (French)

Paris Group B (French)Italian

K=5


Visualisation – Evolution of Node Connectivity http://www.cl.cam.ac.uk/~ey204/Haggle/Vis/mobility.html


Other Community Detection Methodologies

• Betweenness [Newman04]

• Modularity [Newman06]

• Information theory[Rosvall06]– Most literature is in social anthropology– Some in physics (AS level topology and web

interconnect topology)– Future is to look at social net (myspace,

linkedin, orkut, yahoo groups, facebook)– And user contributed tags (del.icio.us, etc)


Centrality in Temporal Network

• Simulate flooding over the temporal graph

• Uniform source/destination and temporal traffic distribution

–Count number of times each node would be on shortest delay deliveries

–Higher the count, more central node

–Analogue to Freeman centrality

• Need to threshold over lifespan of “link” in temporal graph


In-Group Centrality (Reality)

Group A

Group DGroup C

Group B


2. And those online societies…

• There’s a lot – you probably are on 1– Hey, maybe you left several too

• Anyone remember Usenet– Rec.*, Alt.* etc– The Well and Bboards


Lots of Social Networks

• High Functionality– Facebook, Myspace– Content and applications shared as well as

links and interests

• Pure Discovery– Linkedin, Orkut– Mainly useful for finding work, Q&A etc


Lots of Real Social Groups

• Home, Family

• Work, Activities, Progress, Results, Admin

• Sport/Wellbeing

• Health, doctors etc

• Travel, Entertainment, shared interest

• Financial (pension schemes, saving, advice)


Internet Based Systems

• Are large – Facebook cites 100s millions– Are connected– Serve some useful functions– Are differentiated (by functions, content)

• Revenue can come from – advertisement– Or from spinoff activities– Or just bundled by ISP (remember AOL online

and MiniTel


My LinkedIn Stats…


See Locale and Industry for me:-


Note two net characteristics

• Very high node degree– 150 – is well known in sociology to be social group size

• Very fast discovery– Within 1 month of being in Paris, already shows my

network

• Note this is on infrastructure/email/web based social net where I get about 1 join request per day– Mobile net would automate these joins – see next


Mobile Social Nets

• Mobile social net tools/systems abound since recently• First generation• dodgeball

– Nokia sensor, and others centered on cellular providers

• Newer, trying to get into dating etc– aka aki– mobiluck– meetmoi– imity

• Very new:- immediately useful (save phone state etc)– Mocospace– mig33– zyb


But “sad” and geeky


What can we use to Hook?

• Big Bug: – contradiction between social people meet people in “reallife” (RL)

• and sad geeks, who inhabit virtual world:)• Possible escape routes:

– games (move from console to smart phone to console)– music (listen and create)– couch potatoe-able escape causes:– view sport via multiple viewpoints and discuss

• Other hooks (usefulness!):– disasters (data loss, infrastructure down)– cheap– s/w distribution


Building Mobile Network

• Discover Common Locations (space) – Familiar Strangers

• Fellow Commuters– Use location service

• In network, or seperate

• Discover Common Friends by Activity (time)– Friend of friend

• By exchange of address books

– Common interest• subscription or del.icio.us tags in common


Content Distribution on Mobile Network

• Use infrastructure• GPRS/Edge/3G

– Or WiFi Hotspots

• Ad Hoc Networking – MANET forward via others smart phones

• Delay Tolerant Networking– Store, Carry, Forward

• In all these, need to know where humans are and when


The people are the network

• The mobility of people is input to network design– Either to place capacity for infrastructure,– Or for setting up regular, or opportunistic forwarding

paths over users devices– Need to distunguish between people who are social

butterflies (hub for idle chit chat/gossip)• Meet lots of people

– and people who are central for forwarding a lot of information

• Meet people and exchange a lot of data for them often


Problems

• Fear • Users fear:

– spam, loss of euros, loss of battery life, identity theft, denial of service,

• Too many interrupts to life– Too little privacy

• Does community (e.g. shared interest or locale) help? …


What do we Trust

• Users trust members of their tribe a lot more than strangers, but…

• To build a network, usually we want bi-directional “links”

• Are trust relationships associative, commutative, transitive, reflexive?

• Not in social relationships, usually:


Can we build incentives?

• Perhaps additional capacity (mutual benefit) is enough? – potential capacity for DTN use of multihop,

store-carry-forward much more than cellular – But delay highly unpredictable and possibly

hours or days (although we tolerate this for email)

– Can we enhance delay and trust with infrastructure?


Use infrastructure to bootstrap

• Yes, infrastructure helps a lot – Can bootstrap social network from

infrastructure– Can bootstrap payment and identity (SIM card

and cellular or WiFi contract) from infrastructure

– Can use nodes with infrastructure access to shortcut, and improve delay distributions

• Trade off against a cost


Could even monetize call time

• E.g. I swap carrying mp3s for you, for you giving me minutes of voice call time on (say) SFR or Vodafone

• I can ask infrastructure to help with– Location service– Channel/spectrum allocation for MANET/DTN

Users– Proof of Identity– Authenticating “currency” (a “mint”)


Thank you• Merci pour votre attention

• Questions?

• [email protected]

• Next slides show that we can build social networks in a decentralized way

• Without any infrastructure – • future work would include hybrid with infrastructure (to

boot, and to provide persistence) and garbage collection

mailto:[email protected]


More Future Work…

• Privacy and trust (including incentives forwarding).

• Absolute location based, versus co-location based forwarding

• Other layer social network based forwarding, e.g.• Content (url distance, or meta, e.g. del.icio.us

tag) based forwarding• Myspace, Orkut, etc


[email protected]

et merci a CNRS et Thomson

mailto:[email protected]

MSRC, Cambridge, UK Dec 7, 2007 The Network is the People or The People are the Network (les gens...

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