网络专题选讲华中科技大学电子与信息工程系程文青 [email protected]...

网络专题选讲

华中科技大学电子与信息工程系

程文青 [email protected]

2013 年 1 月

社会网络应用专题选讲

华中科技大学电子与信息工程系互联网技术与工程研究中心

黑晓军Email: [email protected]

Web: http://itec.hust.edu.cn/~heixj2013.1

《网络专题选讲》 -3-

Outline

Introduction Case study

Traffic transportNetTube: Exploring Social Networks for Peer-to-Peer Short Video Sharing, 2009

IncentiveP2P Trading in Social Networks: The Value of Staying Connected, 2010

RecommendationCircle-based Recommendation in Online Social Networks, 2012

Internet Topology

Introduction

我们生活在一个关系的社会

5

社会网络应用

6

Friend network in Facebook

7

Co-authorship network

8

Co-authorship in network science

9

Ingredient networks

10

911事件——犯罪网络

11

Social Networking （ General public）

Social Networking （ Academia）

专著

研究现状主要研究机构

国外： MIT 、 Stanford 、 Maryland 、 USC 、 HP 、 Michigan 国内： IBM 中国研究院、微软亚洲研究院、中科院、中国传媒大学

、清华大学、南京大学近年来社会网络成为国内外研究热点

美国国家科学基金会（ NSF ）将社会计算研究领域提供专项资金（ 2010 ）美国计算机协会 ACM ， Workshop on Social Network Mining and

Analysis(2007~2012) WWW 会议成立“ Social Network and Web2.0 Track” 论坛 (2009) SIGCOMM ， ACM SIGCOMM Workshop on Online Social

Networks(2009~2012) EuroSys, Workshop on Social Network System(2009~2012) 互联网测量会议（ IMC ），海量数据仓库国际会议（ VLDB ），信息与知识管

理（ CIKM ）大量关于社交网络文章全国网络科学论坛（ 2004 ～ 2012 ），全国复杂网络会议（ 2005 ～ 2011 ）

17

NetTube: Exploring Social Networks for Exploring Social Networks for Peer-to-Peer Short Video Peer-to-Peer Short Video SharingSharing

Xu Cheng and Jiangchuan LiuXu Cheng and Jiangchuan LiuSchool of Computing ScienceSchool of Computing Science

Simon Fraser UniversitySimon Fraser University

British Columbia, CanadaBritish Columbia, Canada

October 2009October 2009

IEEE INFOCOM, 2009

Background (1)Background (1)

Social Networked Media Sharing – new killer Internet application Since 2005 Rich user-generated content (UGC) sharing Social networks

Among users Among videos

Changing the popular culture

Background (2)Background (2)

YouTube – a representative Popular

Market share of around 43% More than six billion videos viewed in January 2009 Consumed as much bandwidth as the entire Internet in

2000 3rd visits among all Internet sites (after Google and Yahoo)

Fast growing 20% growth rate per month 15 hours of new videos are uploaded

every minute

Motivation (1)Motivation (1)

The YouTube Crisis – all other sites’ challenge Severely hindered by client/server architecture Bandwidth costs

Consumed as much bandwidth as the entire Internet in 2000

$1 million a day for server bandwidth! Sold to Google for $1.65 billion in Nov. 2006

Performance and scalability “Slow” among the surveyed sites by Alexa.com

Motivation (2)Motivation (2)

Peer-to-peer (P2P) – alternative to Client/Server New generation of communication paradigm

Each peer contributes its bandwidth to serve others

Scale well with larger user base More users, more resources contributed

Success already seen in BitTorrent, eMule, eDonkey (file sharing) Video broadcasting …

P2P架构

没有永远在线的服务器任意主机可以同另一个主

机进行通信节点可以间歇性的连入系

统， IP 地址可能会变化

23

peer-peer

对等网络流媒体系统两大设计空间

如何形成重叠网络？如何传输内容？

现有体系结构树状拓扑 + 推式内容传输

ESM, Yoid, CoopNet, SplitStream, Bullet, Chunkspread …

网状拓扑 + 拉式内容传输

-24-《网络专题选讲》

网状 - 拉式对等网络流媒体系统

这类系统非常类似于 BitTorrent


节点软件结构

双缓存积极下载 vs 保守下载处理丢失的数据块缓存控制


缓存映像 (buffer map)

缓存映像反映了节点缓存所拥有的数据块信息此映像可以被用来评估用户的播放质量


对等网络中的问题

内容组织和搜索内容传输信誉、激励及安全相关问题

28

CoolStreamingCoolStreaming

The first practical large-scale P2P NetTV Origin of data-driven mesh design

With many follow-ups: PPLive, PPStream, UUSee …

X. Zhang, J. Liu, B. Li, and T.-S. P. Yum, CoolStreaming/DONet: A Data-driven Overlay Network for Live Media Streaming, IEEE INFOCOM'05, March 2005. >800 citations

J. Liu, S. G. Rao, B. Li, and H. Zhang, Opportunities and Challenges of Peer-to-Peer Internet Video Broadcast, Proceedings of the IEEE, Vol. 96, No. 1, pp. 11-24, January 2008.

Data-Driven MeshData-Driven Mesh

Core operations Every node periodically exchanges data availability

information with a set of partners Then retrieves unavailable data from one or more partners,

or supplies available data to partners Easy to implement

no need to construct and

maintain a complex global structure Efficient

data forwarding is dynamically

determined according to data availability Robust and resilient

adaptive and quick switching among multi-suppliers

Challenges and Opportunities (1)Challenges and Opportunities (1) Challenges – Drastically different statistics

1.5 year measurement of 5 million videos http://netsg.cs.sfu.ca/youtubedata/

Short video clips – stability 99.6% are less than 700 seconds “I don’t want to wait for 30 seconds for

a two-minute video!” Searching for sources

High churn rate: join/leave system

Huge number of videos – scalability Highly skewed Inefficient for unpopular videos Very few users watch the same one

Challenges and Opportunities (2)Challenges and Opportunities (2)

Opportunities – Social networks

No longer independent – videos have related videos Small-world – strong clustering Important role

NetTube Design (1)NetTube Design (1)

Bi-layer overlay network Lower-layer – per-video

Download and uploading Peers stay in previous overlays as

sources Larger and more stable

Upper-layer – social network Connected by the same peers in

different lower-layer overlays Conceptual relation for searching Social network brings similar peers

closer Clustering Efficient searching


Bloom filter based indexing An efficient approach to keep track of peers’

cached videos Bloom filter

An m-bit array using k hash functions Space-efficient

Scalable indexing table Fast searching Table size is scalable with the number of videos Search locally and search in the upper-layer overlay

Social network clustering the similar video


Transmission scheduling:

From which partner to fetch which data segment ? Constraints

Data availability Playback deadline Heterogeneous partner bandwidth

Rarest-first (BitTorrent’s) doesn’t work !


Variation of Parallel machine scheduling NP-hard

Conventional Heuristics Message exchanged

Window-based buffer map (BM): Data availability Segment request (piggyback by BM)

Less suppliers first Multi-supplier: Highest bandwidth within deadline first


Short video ?

CODAS: Collaborative Delay-Aware Scheduling


Social network assisted pre-fetching Most peers finish downloading before playback

ends - free time available (about 80 seconds on average)

Using free time to reduce startup delay Prefix pre-fetching

Avoid wasting bandwidth and space Enable multiple pre-fetching

Multiple pre-fetching Accuracy increases greatly Accuracy increases as watch more videos

Pre-fetching among neighbors Easy to implement Social network helps improve efficiency

Performance Evaluation (1)Performance Evaluation (1)

Simulation Configuration

Based on about 7,000 crawled videos Scale to more than 10,000 heterogeneous clients Compare with PA-VoD (MSN Video)

Bandwidth reduction Save significantly more More scalable


Simulation Impact of social network

Find more sources: more than 95% within 2 hops Greatly increase pre-fetching accuracy


PlanetLab experiment Configuration

Maximum 235 PlanetLab nodes

Experiment results Server bandwidth reduction: more than 40% Startup delay: average 2.2 s Playback continuity

SummarySummary

Contribution First social network assisted P2P system for short video sharing IWQoS’08, INFOCOM’09, IEEE Transactions on Multimedia

Techniques Bi-layer overlay network Bloom filter based indexing Social network assisted pre-fetching Collaborative delay-aware scheduling

Evaluation results Greatly reduce server bandwidth

Much lower maintenance cost: $1 million → $60 K Inherently scalable – P2P

Greatly reduce playback delay Satisfying startup delay Continuous playback

P2P Trading in Social Networks: The Value of Staying Connected

Zhengye Liu, Hao Hu, Yong Liu, Keith Ross, Yao Wang, and Markus Mobius

Polytechnic Institute of NYU Dept. of Economics, Harvard Unviversity

43

IEEE INFOCOM, 2010

Outline

Background: P2P Incentive

Networked Asynchronous Bilateral Trading (NABT)

NABT Efficiency Theory

NABT Simulations

Conclusions

44

P2P Apps: BitTorrent

45

P2P Apps: Skype

46

Peer-Assisted Video Streaming

Large scale deployments on Internet thousands of live/on-demand channels millions of world-wide users daily

Leading P2P Video Companies

CoolStreaming PPStream PPLive Sopcast UUSee

47

3

6

5

4

1

2

Major P2P Issues Traffic localization

P4P Security

Attacks on Attacks from

Lack of uniform API Incentives for peers to contribute resources

48

Partially Successful P2P Incentive

BitTorrent is popular 50+ client implementations Dozen public trackers 5-10 million users

Why BitTorrent?

First generation P2P applications: Gnutella 70% of users are free-riders

Second generation P2P applications: BitTorrent

P2P designP2P design ResourcesResourcesIncentivesIncentives+

49

The BitTorrent Incentive

Implementation of incentive: The rich play/trade with the rich

To get files faster…contribute more bandwidth

50

BitTorrent: Tit-for-tat

(1) Alice tries sending to Bob. Is he rich?

(2) Alice becomes one of Bob’s top-four providers; Bob reciprocates.(3) Bob becomes one of Alice’s top-four providers.

(0) Everyone nominally has four trading partners

Tit-for-Tat: Live P2P Video

“LayerP2P: Using Layered Video Chunks in P2P Live Streaming”,

Z. Liu, Y. Shen, K.W. Ross, S. Panwar, Y. Wang,

IEEE Transactions on Multimedia, November 2009.

“LayerP2P: Using Layered Video Chunks in P2P Live Streaming”,


IEEE Transactions on Multimedia, November 2009.

To get better video quality…contribute more bandwidth

LC11

LC21

LC31

LC12

LC22

LC32

LC13

LC23

LC33

LC14

LC24

LC34Layer 3

Layer 2

Layer 1

“Substream Trading: Towards an Open P2P Live Streaming System”,


Inter Conf on Network Protocols (ICNP), October 2008

“Substream Trading: Towards an Open P2P Live Streaming System”,


Inter Conf on Network Protocols (ICNP), October 200852

Limitations of Tit-for-Tat

Tit-for-Tat is synchronous trading Alice and Bob can trade if and only if they

simultaneously have data for each other in a short time period

Tit-for-Tat == Barter ( 物物交换 ) in primitive economy

Barter is highly inefficient fails if lack of “double coincidence of wants” failure example:

Tit-for-tat does not provide incentive for seeding 53

Currency-based Trading

Currency improves trading efficiency in modern economy

Asynchronous trading regulated by money users accumulate for providing services and later spend for acquiring services

54

Major Issues/Solutions

Cheating Counterfeit Dispute Resolution

Solutions:

Banking SystemMarket RegulationTrading PolicyCourt SystemLaw Enforcement……

55

Global Currency in P2P? Peers trade with each other using digital cash

earn cash by contributing resources to provide services to other peers,

pay cash to consume services provided by other peer.

Heavyweight coordination infrastructure needed banking/regulation/court/enforcement hard to justify for P2P trading goods carrying low value.

Only limited research attempts, no large-scale deployment

56

Desirable P2P Incentive Mechanism

High Trading Efficiency trade asynchronously trade with many peers trade diverse set of goods/services

Cheating-proof isolate and punish cheaters prohibit collusions

Low-degree of Coordination light-weight and distributed protocols low management cost

57

Outline




NABT Simulations

Conclusions

58

Alternative Trading Systems in Social Networks

Asynchronous Trading exploit trust between friends allow debt: providing a service without immediate payment

Networked Trading exploit trust in network of friends trade with indirect friends

“Trust and social collateral”.

Dean Karlan, Markus Mobius, Tanya Rosenblat, and Adam Szeidl.

Quarterly Journal of Economics, 2008.

“Trust and social collateral”.

Dean Karlan, Markus Mobius, Tanya Rosenblat, and Adam Szeidl.

Quarterly Journal of Economics, 2008.59

Friendship as Trading Collateral ( 抵押 )

Resolve cheating/disputes:

Terminate friendship!

60

P2P Trading in Social Networks

Networked Asynchronous Bilateral Trading (NABT) Social network: peers belong

to an underlying social network Pair-wise credit: friends

maintain pair-wise credits Asynchronous trading: peers

can use their credits anytime they want Credit limit: each peer sets a credit limit for each of

its friends Networked trading: peer trades with a remote peer

by transferring credits through a chain of friends links. 61

Async Trading Between Direct Friends

A pair of friends maintain local credit balance bij = amount of credits

that j owes i bij=-bji

update balance upon services

Control risk of defaulting Cij = credit limit for j set by i

- Cji ≤ bij ≤ Cij incentivizes users

bAB

bBA

AliceBob

+ ∆

- ∆

62

Networked Trading via Intermediaries To access service on a remote peer

1. find a path of friend links in social network

2. arrange a series of credit transfers along path

3. intermediaries update credit balances with upstream and downstream friends, and break even

4. remote peer provides requested service

bAB+∆

AliceBob

, bBA-∆, bBC +∆

bCB-∆

Charlie

63

NABT Issues NABT is decentralized, and effective for

resolving disputes.

But Is NABT efficient?

How to set credit limits Cij ?

Can users free-ride in NABT?

64

Outline




NABT Simulations

Conclusions

65

NABT Efficiency Single trade can be exercised if and only if a

credit transfer can be arranged subject to social network connectivity obey credit limit on each social link

Multiple trades coupled through the underlying social network later trades work with credit balance resulted

from earlier trades concurrent trades compete for credit transfer

66

NABT Efficiency Model Given:

underlying social network: credit limits as link weights: service demand matrix:

: cost charged by user k to serve user l. Find

credit transfer flows for all demands : credit flow for demand d on social link <i,j> credit flow conservation on intermediaries

resulting credit balance bounded by credit limits

67

NABT Credit Flow Routing

Similar to classical network flow problem, but: credit balance on link can be negative credit flows in opposite directions cancel

Example: Circular Service Demands: A wants a file on B, B

wants a file on C, and C wants a file on A

B

A C

credit routing scheme 1

bBA=1

bCB=1bAC=1

B

A C

credit routing scheme 2

bBA=0

bCB=0bAC=0

68

Balanced Demand

For each user k, total service he provides (regardless of receivers) equals total service received (regardless of providers)

Theorem 1: Any balanced demand can be executed as long as users involved in the demand sets are connected.

NABT is as efficient as global currency networked Tit-for-Tat: peers play tit-for-tat with

whole network instead of another peer 69

Unbalanced Demand

For at least one user, service contribution does not equal to service consumption. net-service contribution: service sources: service sinks: aggregate net-service imbalance

70

Extended Social Network

augment social network with a virtual source, a virtual sink, virtual links

Example aggregatenet-service imbalance

71

Efficiency with Unbalanced Demand

Theorem: An unbalanced demand is executable iff the min-cut between the source s+ and sink s- in extended social network is greater than or equal to the aggregate net-service imbalance.

What matters: underlying social network topology credit limits on social links service imbalance between a user

and whole network

What does not matter: service imbalance between individual pairs of users

72

Dynamic Payment Routing

Time is slotted

Demands are now sequential H(1), H(2),…

Suppose we succeed at executing H(1),…,H(k-1).

Theorem: To successfully execute H(k), we do not have to worry about how we executed H(1),…,H(k-1).

73

Outline




NABT Simulations

Conclusions

74

Preliminary NABT Protocol Design

On-demand credit flow routing locate service providers send out credit-transfer request through controlled flooding request propagates along friends links with enough credit

space When request hits one providers, it sends back reply

through reverse path to establish credit transfer on intermediaries.

Complete credit transfer and service Dynamic credit-limit setting

increase credit-limit linearly after each fulfilled transaction decrease credit-limit multiplicatively after each

unfulfilled/disputed transaction

75

Simulation Study Trading with global currency (GCT):

Global currency and a centralized bank Each peer has Bi initial credits and each file costs one credit If peer i downloads a file from peer j, peer i pays 1 credit to peer j

Synchronous Trading (ST): Two peers can trade if and only if they can supply files to each

other simultaneously If peer i downloads a file from peer j, peer j will download a file

from peer i. Two-hop NABT:

Peers are connected in an underlying social network A requesting peer requests files from its friends (one-hop friends)

and the friends of its friends (two-hop friends) If peer i downloads a file from peer j within two hops, peer i passes

1 credit to peer j

76

Simulation Setup Peer profile

Social network with a topology collected from MySpace Totally 10,000 peers Peer upload bandwidth

37% Ethernet users (1.2Mbps) + 63% residential users (400 kbps)

Willingness for sharing 10% content-rich peers (1,000 files) + 90% content-scarce

peers (50 files) Online and offline

Markov ON-OFF process (On time = Off time = 12 hours)

File profile Totally 10,000 different files Files are small and have the same size of 3MB File popularity follows a Zipf distribution

77

Trading Efficiency

Request success ratio: The ratio of fulfilled requests to the total number of requests

CDF of request success ratio78

Importance of Trading Intermediaries

CDF of request success ratio for the systems with and without intermediaries79

Service Differentiation of NABT

Relation between request success ratio and upload contribution (in terms of number of uploaded files)

80

Conclusion

NABT -- a new P2P trading paradigm over social networks exploits trust between friends, and friends network trade asynchronously, and over network, light-weight, distributed

NABT is efficient almost as efficient as global currencies support networked tit-for-tat topology and credit limits matters memoryless

81

Open Research Issues incentives for intermediaries isolate and punish cheators dynamic credit-limit setting heterogeneous NABT market

diverse set of services exchange ratio between pair-wise credits deal-making

… …

82

Take Away Messages Asynchronous incentives are critical

for taking P2P to the next level

Async incentives require money

The future of P2P may lie in social networks

83

84

Circle-based Recommendation in Online Social Networks

Xiwang Yang, Harald Steck*, and Yong Liu

Polytechnic Institute of NYU

* Bell Labs/Netflix

84

ACM KDD 2012

85

Outline Background & Motivation

Circle-based RS Trust Circle Inference Trust Value Assignment Model Training

Evaluation Conclusion & Future work

85

Social Recommenders Everywhere

86

Collaborative Filtering (CF) Most Used and Well Known Approach for

Recommendation

Finds Users with Similar Interests to the target User

Aggregating their opinions to make a recommendation.

87

User Based Collaborative Filtering

TargetTargetCustomerCustomer

AggregatorAggregator

Prediction88

,u u iu

uu

w r

w

Item-based Collaborative Filtering

89

Item-Item Collaborative Filtering

AggregatorAggregator

Prediction90

,i u ii

ii

w r

w

Matrix Factorization (BaseMF) [NIPS08]

91

Introduced by R. Salakhutdinov and A. Mnih Probabilistic matrix factorization. In NIPS 2008

Model based approach Latent features for users

Latent features for items

P and Q have normal priors

0i dP R

0u dQ R

Matrix Factorization (BaseMF)

92

Prediction Model

Objective Function P and Q have normal priors

,

,

2 2, ,

2,

ˆ( | , , ) [ ( | ), ]

[ ( | ), ]

Ru i

Ru i

I

R u i u i Rall u all i

ITu i m u i R

all u all i

P R P Q N R R

N R r Q P

ˆ TmR r QP

2 2 2, ,

( , ) .

1 ˆ( ) (|| || || || )2 2u i u i F F

u i obs

R R P Q

Related Work-Social Recommender

Social Recommendation (SoRec) Model CIKM’08 Factorizing social trust matrix together with user rating matrix

Social Trust Ensemble (STE) Model SIGIR’09 User’s rating influenced by social friends

SocialMF Model RecSys’10 User’s latent feature (taste) influenced by social friends Handle trust propagation in social network

Using whole trust network for item rating prediction

93

SocialMF [RecSys2010]

Social Influence behavior of a user u is affected by his direct neighbors .

Latent factor of a user depend on his neighbors. is the normalized trust value. Prediction Model: Objective:

94

uF

*,u vS

2, ,

( , ) .

* *, ,

2 2

1 ˆ( )2

( )( )2

(|| || || || )2

u i u iu i obs

Tu u v v u u v v

all u v v

F F

R R

Q S Q Q S Q

P Q

Proposed Improvements for Current Social Recommender

Social networks include multiple circles A more refined social trust information—richer information Incorporate circle information in Social Recommender Use trust circles specific to an item category when predict

rating in this category e.g. Trust Circle of “Music”, Trust Circle of “Cars”, etc

95


96

Existing circles (Google+, Facebook) not corresponding to an item category


In existing multi-category rating datasets, no circle information User trusts different subsets of friends in different domains (Cars,

Music…) User trusts different friends differently, related to friend’s

expertise value

Should use trust circle specific to item category97

98




98

Trust Circle Inference

User v is in inferred circle c of u iff u trust v in original social network and both of them have rating in category c

9999

Original Social Network

Inferred circle for category C1



100




100

Trust Value Assignment

CircleCon1: Equal Trust

101

Trust Value Assignment

CircleCon2: Expertise-based Trust assign a higher trust value or weight to the friends that are

experts in the circle / category.

102

CircleCon2: Expertise-based Trust

Variant a: Expertise based on number of ratings in a circle

103

CircleCon2: Expertise-based Trust

Variant b:

104

Dw records the proportions of ratings user w assigned in all categories. It reflects the interest distribution of w cross all categories

( ) ( ) ( )c c cv v vE N

CircleCon3: Trust Splitting

Trust due to followee’s rating in one category Likelihood u2 trusts u1 in C1, C2 ? Infer likelihood proportional on u2’s number of ratings in C1 and C2. Assign trust value in a category proportional to the likelihood u2 trusts

u1 in a category

105

Original trust link trust link in c1 trust link in c2

CircleCon3: Trust Splitting

Normalize across followees

106

1 2

1 19, 1c c

u uN N 1 2

2 1 2 1

( ) ( ), ,0.9, 0.1c c

u u u uS S

107




107

Model Training Training with ratings from each category

Predict user’s rating in category c Input rating: rating in category c Input social network: Circle c

108

( )0

ci

( ) ( ) ( ) ( ) ( )*

( ) ( ) 2, ,

( , ) .

( ) ( )* ( ) ( ) ( )* ( ), ,

( ) 2 ( ) 2

( , , , )

1 ˆ( )2

( )( )2

(|| || || || )2

c c c c c

c cu i u i

u i obs

c c c c c c Tu u v v u u v v

all u v v

c cF F

L R Q P S

R R

Q S Q Q S Q

P Q

( ) ( ) ( ) ( ),

ˆ c c c c Tu i m u iR r Q P

is the number of items in category c

Solved by gradient descent

is social information weight

( )0 0( ) ( ),

ci d u dc cP R Q R

Model Training

Training with ratings from each category

109

Model Training

Training with ratings for all categories Predict user’s rating in category c Input rating: rating from all categories Input social network: Circle c

110

0 0( ) ( ),i d u dc cP R Q R

( ) ( ) ( ) ( )*

2, ,

( , ) .

( ) ( )* ( ) ( ) ( )* ( ), ,

( ) 2 ( ) 2

( , , , )

1 ˆ( )2

( )( )2

(|| || || || )2

c c c c

u i u iu i obs


all u v v

c cF F

L R Q P S

R R

Q S Q Q S Q

P Q

111




111

Epinions Data

112

Performance Metrics

113

2, ,( , )

ˆ( )

| |test

u i u iu i R

test

R RRMSE

R

, ,( , )ˆ| |

| |test

u i u iu i R

test

R RMAE

R

Training with per-category ratings

114

Training with per-category ratings

115

( ) ( ) ( ) ( ) ( )*

( ) ( ) 2 ( ) 2 ( ) 2, ,

( , ) .

( ) ( )* ( ) ( ) ( )* ( ), ,

( , , , )

1 ˆ( ) (|| || || || )2 2

( )( )2

c c c c c

c c c cu i u i F F

u i obs


all u v v

L R Q P S

R R P Q

Q S Q Q S Q

Training with ratings from all categories

116

CircleCon3 of training with per-category rating


117


118

Summary

Propose a novel Circle-based Social Recommendation framework Split original social network to different circles, one circle

corresponding to one item category User trusts different subsets of friends in different domains(Cars,

Music…) User trusts different friends differently, based on friend’s expertise

Outperforms the state-of-the-art social collaborative filtering algorithms

Show the promising future of circle-construction techniques in Social Recommender

119

小结

120

Social networking has been changing the way which people communicate!

Reading List Lada Adamic, Social Network Analysis,

https://class.coursera.org/sna-2012-001/wiki/view?page=syllabus

World By David Easley and Jon Kleinberg, Networks, Crowds, and Markets Reasoning About a Highly Connected, Cambridge University Press, 2010 http://www.cs.cornell.edu/home/kleinber/networks-book/

Xu Cheng and Jiangchuan Liu, "NetTube: Exploring Social Networks for Peer-to-Peer Short Video Sharing", IEEE INFOCOM, 2009.

Zhengye Liu, Hao Hu, Yong Liu, Keith Ross, Yao Wang, and Markus Mobius, “P2P Trading in Social Networks: The Value of Staying Connected”, in the Proceedings of IEEE Conference on Computer and Communications IEEE INFOCOM, 2010

Xiwang Yang, Harald Steck and Yong Liu, “Circle-based Recommendation in Online Social Networks ”, in the Proceedings of ACM SIGKDD Conference on Knowledge Discovery and Data Mining (KDD 2012), Long Paper, August, 2012

-121-

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Transcript of 网络专题选讲 华中科技大学 电子与信息工程系 程文青 [email protected]...

网络专题选讲华中科技大学电子与信息工程系程文青 [email protected]...

Transcript of 网络专题选讲华中科技大学电子与信息工程系程文青 [email protected]...