This document discusses how social metrics like interest similarity and centrality can be exploited in content distribution. It focuses on two key social metrics: interest similarity and centrality. For interest similarity, frameworks are presented for defining and measuring interest similarity between users and identifying interest-based groups. The use of interest similarity is shown to improve content replication and dissemination. For centrality, betweenness centrality and its destination-aware variant conditional betweenness centrality are examined for applications like content placement and routing. Simulation results demonstrate that centrality metrics can effectively guide these processes. Both ego-centric and socio-centric computation of centrality are also evaluated.
Toglebox aims to help people that live alone* watch TV with their friends & family (who live on the other side of the country).
Our premise is that if we can help the average person watch 1-of-their-4 hour TV-watching-habit with a friend or family, they will get the required 'social interaction' to help combat some of the difficulties of living alone.
---
*There are 26.4 million households in the UK and 29% consist of only one person.
Introduction aux systèmes de recommandation : filtrage collaboratif, filtrage par le contenu, recommandation de livres et de lectures.
Présentation dans le cadre des journées ARS2017, Université de la Manouba (Tunis)
User Identity Linkage: Data Collection, DataSet Biases, Method, Control and A...IIIT Hyderabad
Online Social Networks (OSNs) are popular platforms for online users. Users typically register and maintain their accounts (user identities) across different OSNs to share a variety of content and remain connected with their friends. Consequently, linking user identities across OSN platforms, referred to as user identity linkage (UIL) becomes a critical problem. Solving this problem enables us to build a more comprehensive view of user’s activities across OSNs, which is highly beneficial for targeted advertisements, recommendations, and many more applications. In the thesis, we propose approaches for analyzing data collection methods, investigating biases in identity linkage datasets, linkage of user identities across social networks, control-ability of user identity linkage, and application of user identity linkage solutions to solve related problems.
2010 June 13
Keynote talk given at the
Workshop for Modeling Social Media
ACM Hypertext 2010 Conference
Presenter: Ed H. Chi
Talk Title:
Model-driven Research for Augmenting Social Cognition
Short Abstract:
Model-driven research seeks to predict and to explain the phenomena in systems. The drive to do this for social computing research should further our understanding of how these systems evolve and develop. I will illustrate how we have modeled the dynamics in the popular social bookmarking system, Delicious, using Information Theory. I will also show how using equations from Evolutionary Dynamics we were better able to explain what might be happening to Wikipedia's contribution patterns.
This paper reports about progress in two areas towards quantum computing architectures with elements inspired from biological controls, as proposed in an earlier paper. The first area is about exploiting mathematical results in coloured algebras, which, combined with the colouring of particle flows, would reduce the decoherence and enhance the decidability in the quantum processing elements; definitions are being recalled, with the required assumptions and results. The second area is to provide experimental results, and a patented biological feedback process in synapse , about light and acoustic excitations in a live animal species to enhance reactivity; the experimental set-up is characterized , the measurement results provided, and the implications are explicated for quantum processing elements approximating a synapse. A paragraph on open issues explains how the results in the two areas will be combined and will help in the design a very early compiler version.
COLOURED ALGEBRAS AND BIOLOGICAL RESPONSE IN QUANTUM BIOLOGICAL COMPUTING ARC...ijcsit
This paper reports about progress in two areas towards quantum computing architectures with elements inspired from biological controls, as proposed in an earlier paper. The first area is about exploiting mathematical results in coloured algebras, which, combined with the colouring of particle flows, would reduce the decoherence and enhance the decidability in the quantum processing elements; definitions are being recalled, with the required assumptions and results. The second area is to provide experimental results, and a patented biological feedback process in synapse , about light and acoustic excitations in a live animal species to enhance reactivity; the experimental set-up is characterized , the measurement results provided, and the implications are explicated for quantum processing elements approximating a synapse. A paragraph on open issues explains how the results in the two areas will be combined and will help in the design a very early compiler version.
SP1: Exploratory Network Analysis with GephiJohn Breslin
ICWSM 2011 Tutorial
Sebastien Heymann and Julian Bilcke
Gephi is an interactive visualization and exploration software for all kinds of networks and relational data: online social networks, emails, communication and financial networks, but also semantic networks, inter-organizational networks and more. Designed to make data navigation and manipulation easy, it aims to fulfill the complete chain from data importing to aesthetics refinements and interaction. Users interact with the visualization and manipulate structures, shapes and colors to reveal hidden properties. The goal is to help data analysts to make hypotheses, intuitively discover patterns or errors in large data collections.
In this tutorial we will provide a hands-on demonstration of the essential functionalities of Gephi, based on a real case scenario: the exploration of student networks from the "Facebook100" dataset (Social Structure of Facebook Networks, Amanda L. Traud et al, 2011). The participants will be guided step by step through the complete chain of representation, manipulation, layout, analysis and aesthetics refinements. Particular focus will be put on filters and metrics for the creation of their first visualizations. They will be incited to compare the hypotheses suggested by their own exploration to the results actually published in the academic paper afterwards. They finally will walk away with the practical knowledge enabling them to use Gephi for their own projects. The tutorial is intended for professionals, researchers and graduates who wish to learn how playing during a network exploration can speed up their studies.
Sébastien Heymann is a Ph.D. Candidate in Computer Science at Université Pierre et Marie Curie, France. His research at the ComplexNetworks team focuses on the dynamics of realworld networks. He leads the Gephi project since 2008, and is the administrator of the Gephi Consortium.
Julian Bilcke is a Software Engineer at ISC-PIF (Complex Systems Institute of Paris, France). He is a founder and a developer for the Gephi project since 2008.
2019년 파이콘 한국에서 진행된 튜토리얼 자료입니다. 최재식 교수님께서 설명가능인공지능이란 무엇인가에 대해 발표해주신 Part 1 발표자료입니다. 아래 링크를 통해 행사 관련 정보를 확인하실 수 있습니다.
http://xai.unist.ac.kr/Tutorial/2018/
https://github.com/OpenXAIProject/PyConKorea2019-Tutorials
Part 1: https://www.slideshare.net/OpenXAI/2019-part-1
Part 2: https://www.slideshare.net/OpenXAI/2019-lrp-part-2
Part 3: https://www.slideshare.net/OpenXAI/2019-shap-part-3
Toglebox aims to help people that live alone* watch TV with their friends & family (who live on the other side of the country).
Our premise is that if we can help the average person watch 1-of-their-4 hour TV-watching-habit with a friend or family, they will get the required 'social interaction' to help combat some of the difficulties of living alone.
---
*There are 26.4 million households in the UK and 29% consist of only one person.
Introduction aux systèmes de recommandation : filtrage collaboratif, filtrage par le contenu, recommandation de livres et de lectures.
Présentation dans le cadre des journées ARS2017, Université de la Manouba (Tunis)
User Identity Linkage: Data Collection, DataSet Biases, Method, Control and A...IIIT Hyderabad
Online Social Networks (OSNs) are popular platforms for online users. Users typically register and maintain their accounts (user identities) across different OSNs to share a variety of content and remain connected with their friends. Consequently, linking user identities across OSN platforms, referred to as user identity linkage (UIL) becomes a critical problem. Solving this problem enables us to build a more comprehensive view of user’s activities across OSNs, which is highly beneficial for targeted advertisements, recommendations, and many more applications. In the thesis, we propose approaches for analyzing data collection methods, investigating biases in identity linkage datasets, linkage of user identities across social networks, control-ability of user identity linkage, and application of user identity linkage solutions to solve related problems.
2010 June 13
Keynote talk given at the
Workshop for Modeling Social Media
ACM Hypertext 2010 Conference
Presenter: Ed H. Chi
Talk Title:
Model-driven Research for Augmenting Social Cognition
Short Abstract:
Model-driven research seeks to predict and to explain the phenomena in systems. The drive to do this for social computing research should further our understanding of how these systems evolve and develop. I will illustrate how we have modeled the dynamics in the popular social bookmarking system, Delicious, using Information Theory. I will also show how using equations from Evolutionary Dynamics we were better able to explain what might be happening to Wikipedia's contribution patterns.
This paper reports about progress in two areas towards quantum computing architectures with elements inspired from biological controls, as proposed in an earlier paper. The first area is about exploiting mathematical results in coloured algebras, which, combined with the colouring of particle flows, would reduce the decoherence and enhance the decidability in the quantum processing elements; definitions are being recalled, with the required assumptions and results. The second area is to provide experimental results, and a patented biological feedback process in synapse , about light and acoustic excitations in a live animal species to enhance reactivity; the experimental set-up is characterized , the measurement results provided, and the implications are explicated for quantum processing elements approximating a synapse. A paragraph on open issues explains how the results in the two areas will be combined and will help in the design a very early compiler version.
COLOURED ALGEBRAS AND BIOLOGICAL RESPONSE IN QUANTUM BIOLOGICAL COMPUTING ARC...ijcsit
This paper reports about progress in two areas towards quantum computing architectures with elements inspired from biological controls, as proposed in an earlier paper. The first area is about exploiting mathematical results in coloured algebras, which, combined with the colouring of particle flows, would reduce the decoherence and enhance the decidability in the quantum processing elements; definitions are being recalled, with the required assumptions and results. The second area is to provide experimental results, and a patented biological feedback process in synapse , about light and acoustic excitations in a live animal species to enhance reactivity; the experimental set-up is characterized , the measurement results provided, and the implications are explicated for quantum processing elements approximating a synapse. A paragraph on open issues explains how the results in the two areas will be combined and will help in the design a very early compiler version.
SP1: Exploratory Network Analysis with GephiJohn Breslin
ICWSM 2011 Tutorial
Sebastien Heymann and Julian Bilcke
Gephi is an interactive visualization and exploration software for all kinds of networks and relational data: online social networks, emails, communication and financial networks, but also semantic networks, inter-organizational networks and more. Designed to make data navigation and manipulation easy, it aims to fulfill the complete chain from data importing to aesthetics refinements and interaction. Users interact with the visualization and manipulate structures, shapes and colors to reveal hidden properties. The goal is to help data analysts to make hypotheses, intuitively discover patterns or errors in large data collections.
In this tutorial we will provide a hands-on demonstration of the essential functionalities of Gephi, based on a real case scenario: the exploration of student networks from the "Facebook100" dataset (Social Structure of Facebook Networks, Amanda L. Traud et al, 2011). The participants will be guided step by step through the complete chain of representation, manipulation, layout, analysis and aesthetics refinements. Particular focus will be put on filters and metrics for the creation of their first visualizations. They will be incited to compare the hypotheses suggested by their own exploration to the results actually published in the academic paper afterwards. They finally will walk away with the practical knowledge enabling them to use Gephi for their own projects. The tutorial is intended for professionals, researchers and graduates who wish to learn how playing during a network exploration can speed up their studies.
Sébastien Heymann is a Ph.D. Candidate in Computer Science at Université Pierre et Marie Curie, France. His research at the ComplexNetworks team focuses on the dynamics of realworld networks. He leads the Gephi project since 2008, and is the administrator of the Gephi Consortium.
Julian Bilcke is a Software Engineer at ISC-PIF (Complex Systems Institute of Paris, France). He is a founder and a developer for the Gephi project since 2008.
2019년 파이콘 한국에서 진행된 튜토리얼 자료입니다. 최재식 교수님께서 설명가능인공지능이란 무엇인가에 대해 발표해주신 Part 1 발표자료입니다. 아래 링크를 통해 행사 관련 정보를 확인하실 수 있습니다.
http://xai.unist.ac.kr/Tutorial/2018/
https://github.com/OpenXAIProject/PyConKorea2019-Tutorials
Part 1: https://www.slideshare.net/OpenXAI/2019-part-1
Part 2: https://www.slideshare.net/OpenXAI/2019-lrp-part-2
Part 3: https://www.slideshare.net/OpenXAI/2019-shap-part-3
Presentation given to a Finnish eMBA group during their visit at mediaX at Stanford.
Presenters: Kaisa Still, Jukka Huhtamäki
Session chair: Martha G. Russell
Methods to Maximize the well-being and Vitality of Moribund Communitiesijdmtaiir
It has become the primary concern for the
governments to chart effective methods and policies to
revitalize the communities which are on the verge of
extinction, most of which are indigenous. This has become
more relevant and important in an era of liberalization, which
more often adversely affects the welfare of such communities.
In this paper we make an effort to identify and qualify
measures that would revitalize moribund communities and to
quantify them using fuzzy analysis. We come out with concrete
suggestions for the governments and the policy makers which
can be easily put in action.
MOSES: Community finding using Model-based Overlapping Seed ExpanSion
Iscc2011 ioannis stavrakakis_ keynote
1. Exploiting Social Metrics
in
Content Distribution
Ioannis Stavrakakis
National & Kapodistrian University of Athens
Based on works with:
Merkouris Karaliopoulos, Eva Jaho, Panagiotis Pandazopoulos, Pavlos Nikolopoulos, Therapon Papadimitriou
June 30, 2011
ISCC Keynote Talk ‐ June 30, 2011 ‐ Corfu 1
4. Define and measure Interest Similarity
assess overall interest similarity in social groups
assess overall interest similarity in social groups
identify interest‐based structure within social groups
ISCoDE framework3
(commodity)
Similarity community y
User
metrics detection
profiles
algorithms
0
Thematic areas
1st step: user profiles weighted graph
2nd step : weighted graph interest-similar groups
3E. Jaho, M. Karaliopoulos, I. Stavrakakis. ISCoDe: a framework for interest similarity-based community detection
in social networks Third International Workshop on Network Science for Communication Networks (INFOCOM
networks. (INFOCOM-
NetSciCom’11), Apr. 10-15, 2011, Shanghai.
ISCC Keynote Talk ‐ June 30, 2011 ‐ Corfu 4
5. Similarity metrics: PS vs. InvKL (Kullback Leibler)
• Proportional Similarity (PS) 1 M i
PS ( F , F ) = 1 − ∑ F m − F j m
i j
– PS : {Fi , Fj} [0,1] 2 m=1
1
InvKL( F i , F j ) =
• Inverse symmetrized KL divergence
y g M
F im M j F jm
– InvKL : {Fi, Fj} (0,∞)
∑ F m log F j m + ∑ F m log F i m
m =1
i
m =1
F n m , 1≤n≤N, 1≤m≤M : distribution of node n over interest class m
Example with M=2 interest classes and N=2 nodes
• Proportional Similarity (PS) Inverse symmetrized KL divergence (InvKL)
ISCC Keynote Talk ‐ June 30, 2011 ‐ Corfu 5 5
6. Resolution performance
InvKL can identify smaller and
more similar communities than
PS, in a highly similar network
, g y
PS can identify smaller
communities than InvKL, in a
highly dissimilar network
(could argue that this is not very
useful)
ISCC Keynote Talk ‐ June 30, 2011 ‐ Corfu 6 6
7. Can Interest similarity improve network protocols ?
Gain of cooperation for content replication in a group of nodes1
T : tightness metric (=mean
invKL), measuring interest
similarity across group members
i il it b
Percentage of cooperative nodes
1 E. Jaho, M. Karaliopoulos, I. Stavrakakis, “Social similarity as a driver for selfish, cooperative and
altruistic behavior”, in Proc. AOC 2010 (extended version submitted to IEEE TPDS)
ISCC Keynote Talk ‐ June 30, 2011 ‐ Corfu 7
8. Can Interest similarity improve network protocols ?
Content dissemination in opportunistic networks2
C di i i i i i k
Protocols A,B,C are push protocols exercising interest-based forwarding
interest based
(Precision: valuable received / total received --– Recall: valuable received / total potentially valuable generated)
2S.M. Allen, M.J. Chorley, G.B. Colombo, E. Jaho, M. Karaliopoulos, I. Stavrakakis, R.M Whitaker, “Exploiting user
interest similarity and social links for microblog forwarding in mobile opportunistic networks”, submitted to
Elsevier PMC, 2011
ISCC Keynote Talk ‐ June 30, 2011 ‐ Corfu 8
9. 2‐ Betweeness Centrality (BC)
2.1 ‐ Content (service) Migration / Placement
Can BC help provide for a low‐complexity, distributed,
scalable solution?
Destination‐aware vs destination unaware BC
Ego‐centric vs socio‐centric computation of BC
ISCC Keynote Talk ‐ June 30, 2011 ‐ Corfu 9
10. CBC: the “destination‐aware” counterpart to BC
a measure of the importance
of node's u social position : lies on
paths linking others
th li ki th
Betweenness Centrality (u ): portion of all pairs shortest paths of G that
pass through node u
Conditional Betweenness Centrality (u, t ) : portion of all shortest paths of
G from node u to target t, that pass through node u
a measure of the importance
of node's u social position : ability to
control information flow towards 10
target node
10
ISCC Keynote Talk ‐ June 30, 2011 ‐ Corfu 10
11. The content placement problem
Deploy scalable and distributed mechanisms for publishing, placing, moving UG
Service facilities / content within networking structures
Optimal content / service placement in a Graph k‐median
Only distributed, scalable, solutions are relevant
Use local information to migrate towards a better location
Use locally available limited information to solve repeatedly small‐scale k‐
median and repeat
(*)
K. Oikonomou, I. Stavrakakis, “Scalable Service Migration in Autonomic Network Environments,”
IEEE JSAC, Vol. 28, No. 1, Jan. 2010
G. Smaragdakis, N. Laoutaris, K. Oikonomou, I. Stavrakakis, A. Bestavros, “Distributed
G S d ki N L t i K Oik I St k ki A B t “Di t ib t d
Server Migration for Scalable Internet Service Deployment”, to appear in IEEE/ACM T‐ Net.
(2011) , also in INFOCOM2007
ISCC Keynote Talk ‐ June 30, 2011 ‐ Corfu 11
12. Centrality‐based service migration
Consider set of nodes with highest CBC values
C id f d i h hi h CBC l
hosti є Gi
Solve iteratively small‐scale k‐medians
on subgraphs Gi Є G, around the
current facility location of host i Gi
containing the top nodes based
on CBC values
Map the outside demand properly on nodes
in subgraphs Gi I
b h
P. Pandazopoulos, M. Karaliopoulos, I. Stavrakakis, Centrality driven
P Pandazopoulos M Karaliopoulos I Stavrakakis “Centrality‐driven scalable service migration”12
migration ,
23rd International Teletraffic Congress (ITC), Sept. 6‐9, 2011, San Francisco, USA.
12
ISCC Keynote Talk ‐ June 30, 2011 ‐ Corfu 12
13. simulation results: ISP topologies / non‐uniform load
Less than a dozen of nodes is enough!
L h d f d i h!
Demand load : Zipf distribution (with skewness s)
Datasets correspond to different snapshots of 7
ISPs collected by mrinfo multicast tool *
* J.-J. Pansiot, P. Mérindol, B. Donnet, and O. Bonaventure, “Extracting intra-domain topology from mrinfo
probing,
probing ” in Proc Passive and Active Measurement Conference (PAM), April 2010.
Proc. (PAM) 2010
13
ISCC Keynote Talk ‐ June 30, 2011 ‐ Corfu 13
14. Ego‐centric vs socio‐centric computation of BC
Very high rank correlation (Spearman coefficient) !!!
Ego‐ and socio‐ centric metrics identify same subsets
P. Pandazopoulos, M. Karaliopoulos, I. Stavrakakis, “Egocentric assessment of node centrality in physical
network topologies”, submitted to Globecom 2011
topologies
ISCC Keynote Talk ‐ June 30, 2011 ‐ Corfu 14
15. 2‐ Betweeness Centrality (BC)
Betweeness Centrality (BC)
2.1 ‐ Centrality‐driven routing in opportunistic nets
(SimBetTS and BubbleRap use BC values of encounters for content forwarding)
How is performance of centrality‐based routing affected by
Adding or not, destination awareness to BC (BC vs CBC)
Working with ego‐centric vs socio‐centric BC values
Type of contact graph (unweighted vs. weighted) ? Not discussed here
Type of contact graph (unweighted vs weighted) ? Not discussed here
P. Nikolopoulos, et.al. How much off center are centrality metrics for opportunistic routing?
P Nikolopoulos et al “How much off‐center are centrality metrics for opportunistic routing?”,
CHANTS 2011 Workshop (in MobiCom), Sept 23, 2011, Las Vegas
ISCC Keynote Talk ‐ June 30, 2011 ‐ Corfu 15
17. BC vs
BC vs CBC
opt
opt optimal routing through knowledge of contact sequences.
optimal routing through knowledge of contact sequences
BC/CBC up to 30% of messages never reach their destination
about 5 times more hops and 1 day of additional delay
b t 5 ti h d1d f dditi ld l
BC outperforms
CBC in delay
CBC in delay
(due to zero CBC
values when
destination in an
destination in an
unconnected
cluster)
CBC outperforms
BC in hops (up to
50% shorter
50% shorter
paths, due to
selecting more
p p
proper nodes to
forward to)
ISCC Keynote Talk ‐ June 30, 2011 ‐ Corfu 17
19. socio‐ ego‐
socio‐ vs ego‐metrics
strong positive correlation of socio‐ and ego – metrics
(Intel / Content data)
ISCC Keynote Talk ‐ June 30, 2011 ‐ Corfu 19
20. Conclusions
Focused on exploring the impact on two key social metrics on content distribution
Interest Similarity
Centrality
Interest similarity metrics
Highly similar groups can yield high gains in content replication.
Interest similarity –based forwarding improves performance
Worth assessing interest similarity in groups – framework for doing that
Destination‐aware BC :
D ti ti
Very effective in content placement (BC is totally ineffective)
Decreases hop count in opp nets (energy) substantially. Can increase delay
p pp ( gy) y y
Ego‐centric centrality variants (BC/CBC)
Highly rank correlated no performance degradation in content placement /
centrality‐driven
centrality driven content forwarding
forwarding.
Easier to compute
ISCC Keynote Talk ‐ June 30, 2011 ‐ Corfu 20