Social Computing @ Know-Center 
1 
Social Computing in the area of Big Data 
at the Know-Center 
Christoph Trattner 
Know-...
Social Computing @ Know-Center 
2 
Before I will start in this talk I will talk a bit about 
myself and how it happened th...
Social Computing @ Know-Center 
3 
Where do I come from (Austria)? 
. Christoph Trattner 29.8.2014 – PUC, Chile
Social Computing @ Know-Center 
4 
Graz 
. Christoph Trattner 29.8.2014 – PUC, Chile
Social Computing @ Know-Center 
5 
Academic Back-Ground? 
§ Studies Computer Science at Graz University of 
Technology & ...
Social Computing @ Know-Center 
6 
My 
team 
2 
Post-­‐Docs, 
5 
Pre-­‐Docs 
(4 
more 
to 
join 
soon 
J) 
2 
MSc 
studen...
Social Computing @ Know-Center 
7 
Thanks to my Collaborators 
. Christoph Trattner 29.8.2014 – PUC, Chile
Social Computing @ Know-Center 
8 
What is my group doing? 
… we research on novel methods and tools that exploit 
social ...
Social Computing @ Know-Center 
9 
What type of projects are we running? 
COMET NON-K 
FWF EU 
Industry 
Projects 
. Chris...
Social Computing @ Know-Center 
10 
Some industry partners... 
. Christoph Trattner 29.8.2014 – PUC, Chile
Social Computing @ Know-Center 
11 
The Projects 
Project 1: Mendeley – UK Startup (recently acquired by Elsevier): 
Inter...
Social Computing @ Know-Center 
12 
Ok, lets start…. 
. Christoph Trattner 29.8.2014 – PUC, Chile
Social Computing @ Know-Center 
13 
Project 1 
Mendeley – UK Startup (recently acquired by Elsevier): 
Interested in the p...
Social Computing @ Know-Center 
14 
Research Question 1: 
What kind of meta-data is more useful for the task of 
navigatio...
Social Computing @ Know-Center 
15 
Mendeley 
. Christoph Trattner 29.8.2014 – PUC, Chile
Social Computing @ Know-Center 
16 
§ We 
. Christoph Trattner 29.8.2014 – PUC, Chile 
Tags 
Keywords 
Mendeley Desktop
Social Computing @ Know-Center 
17 
Task 
What is the best way to extract hirachies from enties such 
as social tags or ke...
Social Computing @ Know-Center 
18 
Different types of hierarchy induction 
algorithms 
Helic, D., Strohmaier, M., Trattne...
Social Computing @ Know-Center 
19 
Issue (!!!) 
...no literature on what type of hierarchy is best suited 
for the task o...
Social Computing @ Know-Center 
20 
Stanley Milgram 
§ A social psychologist 
§ Yale and Harvard University 
§ Study on...
Social Computing @ Know-Center 
21 
Set Up 
§ Target person: 
§ A Boston stockbroker 
§ Three starting populations 
Neb...
Social Computing @ Know-Center 
22 
Results 
§ How many of the starters would be able to establish 
contact with the targ...
Social Computing @ Know-Center 
23 
Hierarchical decentralized searcher 
Information 
Network 
Hierarchy 
. Christoph Trat...
Social Computing @ Know-Center 
24 
Validation 
§ We compared simulations with 
human click trails of the online Game – 
...
Social Computing @ Know-Center 
Wikipedia Category Label Dataset: 
2,300,000 category labels, 
4,500,000 articles, 30,000,...
Social Computing @ Know-Center 
26 
Hierarchy Creation (2) 
2.) Second type is based on the work of [Muchnik et al. 2007] ...
Social Computing @ Know-Center 
27 
Validation Human Navigators 
. Christoph Trattner 29.8.2014 – PUC, Chile
Social Computing @ Know-Center 
28 
...ok let‘s come back to the Mendeley „problem“... 
. Christoph Trattner 29.8.2014 – P...
Social Computing @ Know-Center 
29 
Are keyword hierarchies more navigable 
than social tag hierarchies? 
Results: 
With s...
Social Computing @ Know-Center 
30 
...ok let‘s move on to some (Social) networking stuff J 
. Christoph Trattner 29.8.20...
Social Computing @ Know-Center 
31 
Project 2 
Blanc Noir – Austrian Startup: Interested in the problem 
of recommending i...
Social Computing @ Know-Center 
32 
Research Question 2: 
To what extent is social network location-based data 
useful to ...
Social Computing @ Know-Center 
33 
How did we answer that question? 
• Major issue: There are no freely available data se...
Social Computing @ Know-Center 
34 
Features 
• In our analysis we focused on content (e.g., common 
interests) and networ...
Social Computing @ Know-Center 
35 
Evaluation 
• We split the dataset in two different kinds of sets (one 
for training a...
Social Computing @ Know-Center 
Results: 
Although the combination of features from 
both social and trading networks did ...
Social Computing @ Know-Center 
37 
Follow-up (1) 
Experiment with location-based 
social network data 
Task: Predict item...
Social Computing @ Know-Center 
38 
Follow-up (2) 
. Christoph Trattner 29.8.2014 – PUC, Chile
Social Computing @ Know-Center 
39 
Recsium Framework 
• Near Real-Time Updates 
• Real Time Recommendations 
• Deals with...
Social Computing @ Know-Center 
40 
Demo - Recsium 
http://recsium.know-center.tugraz.at/recsium/ 
. Christoph Trattner 29...
Social Computing @ Know-Center 
41 
...currently working on 
Location-based services shopping malls, train-stations 
Techn...
Social Computing @ Know-Center 
42 
Project 3 
University of Pittsburgh: Interested on the usefulness 
of Twitter in acade...
Social Computing @ Know-Center 
43 
Research Question 3: 
To what extent is Twitter useful to engage new comers 
(junior r...
Social Computing @ Know-Center 
44 
Dataset 
§ Data: We collected tweets data by searching for the hashtag of four 
confe...
Social Computing @ Know-Center 
Results: 
Junior researchers show the lowest 
group attention, and conversation 
ration am...
Social Computing @ Know-Center 
Results: 
Juniors researchers are less involved 
in the conversation on Twitter than 
any ...
Social Computing @ Know-Center 
Results: 
Retweets and Mentions increase 
over time. Replies and Mentions stay 
steady ove...
Social Computing @ Know-Center 
Results: 
Our analysis reveals a steady growth 
in the communication over twitter 
over ti...
Social Computing @ Know-Center 
Results: 
Eigenvector centrality is the most 
important feature to predict future 
confere...
Social Computing @ Know-Center 
50 
...ok that‘s basically it J 
. Christoph Trattner 29.8.2014 – PUC, Chile
Social Computing @ Know-Center 
51 
...of course there are other projects 
. Christoph Trattner 29.8.2014 – PUC, Chile
Social Computing @ Know-Center 
52 
Thank you! 
Christoph Trattner 
Email: ctrattner@know-center.at 
Web: christophtrattne...
Social Computing @ Know-Center 
53 
Any questions? 
. Christoph Trattner 29.8.2014 – PUC, Chile
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Social Computing in the area of Big Data at the Know-Center Austria's leading competence center for data driven business and Big Data analytics

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Nowadays, social networks and media, such as Facebook, Twitter & Co, affect our communication and our exchange of knowledge more than ever. But which additional benefits can offer social media apart from easy interaction with friends and how can they be used to create additional value for companies and institutions? These are the questions that the area Social Computing at Know-Center addresses in detail.

In this talk we will give a brief overview of industry and non-industry related research projects which we have been involved in recently with my group, Social Computing at the Know-Center, in the context of Big Data and social media. In particular, the talk will highlight specific research project outcomes and work-in-progress that make use of social media data to help people to explore the vastly growing overloaded information space more efficiently.

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Social Computing in the area of Big Data at the Know-Center Austria's leading competence center for data driven business and Big Data analytics

  1. 1. Social Computing @ Know-Center 1 Social Computing in the area of Big Data at the Know-Center Christoph Trattner Know-Center ctrattner@know-center.at @Graz University of Technology, Austria . Christoph Trattner 29.8.2014 – PUC, Chile
  2. 2. Social Computing @ Know-Center 2 Before I will start in this talk I will talk a bit about myself and how it happened that I became Head of the Social Computing Research Area at the Know-Center, Austria’s leading competence center for data driven business and Big Data analytics . Christoph Trattner 29.8.2014 – PUC, Chile
  3. 3. Social Computing @ Know-Center 3 Where do I come from (Austria)? . Christoph Trattner 29.8.2014 – PUC, Chile
  4. 4. Social Computing @ Know-Center 4 Graz . Christoph Trattner 29.8.2014 – PUC, Chile
  5. 5. Social Computing @ Know-Center 5 Academic Back-Ground? § Studies Computer Science at Graz University of Technology & University of Pittsburgh § Worked since 2009 as scientific researcher at the KMI & IICM (BSc 2008, MSc 2009) § My PhD thesis was on the Search & Navigation in Social Tagging Systems (defended 2012) § Since Feb. 2013 @ Know-Center § Leading the SC Area § At TUG: § WebScience § Semantic Technologies . Christoph Trattner 29.8.2014 – PUC, Chile
  6. 6. Social Computing @ Know-Center 6 My team 2 Post-­‐Docs, 5 Pre-­‐Docs (4 more to join soon J) 2 MSc student 2 BSc student DI. Dieter Theiler DI. Dominik Kowald Dr. Peter Kraker . Christoph Trattner 29.8.2014 – PUC, Chile Dr. Elisabeth Lex Mag. Sebastian Dennerlein Mag. Matthias Rella DI. Emanuel Lacic
  7. 7. Social Computing @ Know-Center 7 Thanks to my Collaborators . Christoph Trattner 29.8.2014 – PUC, Chile
  8. 8. Social Computing @ Know-Center 8 What is my group doing? … we research on novel methods and tools that exploit social data to generate a greater value for the individual, communities, companies and the society as whole. Our competences: • Network & Web Science • Science 2.0 • Predictive Modeling • Social Network Analysis • Information Quality Assessment • User Modeling • Machine Learning and Data Mining • Collaborative Systems Our Services: • Social Analytics: Hub-, Expert -, Community -, Influencer -, Information Flow-, Trend (Event) Detection, etc. • Information Quality Assessment • Social & Location-based Recommander Systems • Customer Segmentation • Social Systems Design . Christoph Trattner 29.8.2014 – PUC, Chile
  9. 9. Social Computing @ Know-Center 9 What type of projects are we running? COMET NON-K FWF EU Industry Projects . Christoph Trattner 29.8.2014 – PUC, Chile Non-Industrial Projects FFG ...
  10. 10. Social Computing @ Know-Center 10 Some industry partners... . Christoph Trattner 29.8.2014 – PUC, Chile
  11. 11. Social Computing @ Know-Center 11 The Projects Project 1: Mendeley – UK Startup (recently acquired by Elsevier): Interested in the problem of hirachical concept-based navigation. Project 2: Blanc Noir – Austrian Startup: Interested in the problem of recommending items to users through social data. Project 3: University of Pittsburgh & Several Austrian companies: Interested on the usefulness of Twitter in academic conferences. . Christoph Trattner 29.8.2014 – PUC, Chile
  12. 12. Social Computing @ Know-Center 12 Ok, lets start…. . Christoph Trattner 29.8.2014 – PUC, Chile
  13. 13. Social Computing @ Know-Center 13 Project 1 Mendeley – UK Startup (recently acquired by Elsevier): Interested in the problem of hierarchical concept-based navigation. . Christoph Trattner 29.8.2014 – PUC, Chile
  14. 14. Social Computing @ Know-Center 14 Research Question 1: What kind of meta-data is more useful for the task of navigation in information systems - tags or keywords? Externals involved: • Mendeley, London, UK Helic, D., Körner, C., Granitzer, M., Strohmaier, M. and Trattner, C. 2012. Navigational Efficiency of Broad vs. Narrow Folksonomies. In Proceedings of the 23rd ACM Conference on Hypertext and Social Media (HT 2012), ACM, New York, NY, USA, pp. 63-72. . Christoph Trattner 29.8.2014 – PUC, Chile
  15. 15. Social Computing @ Know-Center 15 Mendeley . Christoph Trattner 29.8.2014 – PUC, Chile
  16. 16. Social Computing @ Know-Center 16 § We . Christoph Trattner 29.8.2014 – PUC, Chile Tags Keywords Mendeley Desktop
  17. 17. Social Computing @ Know-Center 17 Task What is the best way to extract hirachies from enties such as social tags or keywords? What is more useful for navigation – keyword or tag hierarchies? . Christoph Trattner 29.8.2014 – PUC, Chile
  18. 18. Social Computing @ Know-Center 18 Different types of hierarchy induction algorithms Helic, D., Strohmaier, M., Trattner, C., Muhr M. and Lermann, K.: Pragmatic Evaluation of Folksonomies, In Proceedings of the 20th international conference on World Wide Web (WWW 2011), ACM, New York, NY, USA, 417-426, 2011. . Christoph Trattner 29.8.2014 – PUC, Chile
  19. 19. Social Computing @ Know-Center 19 Issue (!!!) ...no literature on what type of hierarchy is best suited for the task of navigation... D. J. Watts, P. S. Dodds, and M. E. J. Newman. Identity and search in social networks. Science, 296:1302–1305, 2002. J. M. Kleinberg. Navigation in a small world. Nature, 406(6798):845, August 2000. . Christoph Trattner 29.8.2014 – PUC, Chile
  20. 20. Social Computing @ Know-Center 20 Stanley Milgram § A social psychologist § Yale and Harvard University § Study on the Small World Problem, beyond well defined communities and relations (such as actors, scientists, …) § „An Experimental Study of the Small World Problem” . Christoph Trattner 29.8.2014 – PUC, Chile 1933-1984
  21. 21. Social Computing @ Know-Center 21 Set Up § Target person: § A Boston stockbroker § Three starting populations Nebraska random § 100 “Nebraska stockholders” § 96 “Nebraska random” § 100 “Boston random” Nebraska stockholders . Christoph Trattner 29.8.2014 – PUC, Chile Target Boston stockbroker Boston random
  22. 22. Social Computing @ Know-Center 22 Results § How many of the starters would be able to establish contact with the target? § 64 out of 296 reached the target § How many intermediaries would be required to link starters with the target? § Well, that depends: the overall mean 5.2 links § Through hometown: 6.1 links § Through business: 4.6 links § Boston group faster than Nebraska groups § Nebraska stockholders not faster than Nebraska random § What form would the distribution of chain lengths take? . Christoph Trattner 29.8.2014 – PUC, Chile
  23. 23. Social Computing @ Know-Center 23 Hierarchical decentralized searcher Information Network Hierarchy . Christoph Trattner 29.8.2014 – PUC, Chile
  24. 24. Social Computing @ Know-Center 24 Validation § We compared simulations with human click trails of the online Game – The Wiki Game (http://thewikigame.com/) § Contains 1,500,000 click trails of more than 500,000 users with (start; target) information. . Christoph Trattner 29.8.2014 – PUC, Chile
  25. 25. Social Computing @ Know-Center Wikipedia Category Label Dataset: 2,300,000 category labels, 4,500,000 articles, 30,000,000 category label assignments Delicious Tag Dataset: 440,000 tags, 580,000 articles and 3,400,000 tag assignments 25 Hierachy Creation (1) Two types of hierarchies were evaluated 1.) First type is based on our previous work § Categorial Concepts: § Tags from Delicious § Category labels from Wikipedia Similarity Graph Latent Hierarchical Taxonomy . Christoph Trattner 29.8.2014 – PUC, Chile
  26. 26. Social Computing @ Know-Center 26 Hierarchy Creation (2) 2.) Second type is based on the work of [Muchnik et al. 2007] Simple idea: Algorithm iterates through all links in the network and decides if that link is of a hierarchical type, in which case it remains in the network otherwise it is removed. Directed link-network dataset of the English-Wikipedia from February 2012. All in all, the dataset includes around 10,000,000 articles and around 250,000,000 links Muchnik, L., Itzhack, R., Solomon S. and Louzoun Y.: Self-emergence of knowledge trees: Extraction of the Wikipedia hierarchies, PHYSICAL REVIEW E 76, 016106 (2007) . Christoph Trattner 29.8.2014 – PUC, Chile
  27. 27. Social Computing @ Know-Center 27 Validation Human Navigators . Christoph Trattner 29.8.2014 – PUC, Chile
  28. 28. Social Computing @ Know-Center 28 ...ok let‘s come back to the Mendeley „problem“... . Christoph Trattner 29.8.2014 – PUC, Chile
  29. 29. Social Computing @ Know-Center 29 Are keyword hierarchies more navigable than social tag hierarchies? Results: With simulations we find that tag-based . Christoph Trattner 29.8.2014 – PUC, Chile Tags Keywords Results: Our Greedy Navigator (= Simulator) needs on average 1-click more with keywords to reach the target node than with tags hierarchies are more efficient for navigation than keywords
  30. 30. Social Computing @ Know-Center 30 ...ok let‘s move on to some (Social) networking stuff J . Christoph Trattner 29.8.2014 – PUC, Chile
  31. 31. Social Computing @ Know-Center 31 Project 2 Blanc Noir – Austrian Startup: Interested in the problem of recommending items to users through social & location-based (social) data. . Christoph Trattner 29.8.2014 – PUC, Chile
  32. 32. Social Computing @ Know-Center 32 Research Question 2: To what extent is social network location-based data useful to predict trades or products in online and offline marketplaces? Externals involved: • Blanc Noir • PUC, Chile Trattner, C., Parra, D., Eberhard, L. and Wen, X.: Who will Trade with Whom? Predicting Buyer-Seller Interactions in Online Trading Platforms through Social Networks, In Proceedings of the ACM World Wide Web Conference (WWW 2014), ACM, New York, NY, 2014. . Christoph Trattner 29.8.2014 – PUC, Chile
  33. 33. Social Computing @ Know-Center 33 How did we answer that question? • Major issue: There are no freely available data sets available • Idea: Crawl data from virtual world of Second Life • Comprises both: • Online Social Network & Location-Based (Social) data • Amazon/eBay alike Marketplace • https://my.secondlife.com/ • https://marketplace.secondlife.com/ . Christoph Trattner 29.8.2014 – PUC, Chile
  34. 34. Social Computing @ Know-Center 34 Features • In our analysis we focused on content (e.g., common interests) and network features (e.g., common interaction partners) Example of network features we used in our analysis . Christoph Trattner 29.8.2014 – PUC, Chile
  35. 35. Social Computing @ Know-Center 35 Evaluation • We split the dataset in two different kinds of sets (one for training and one for testing) • Trained a binary classifier • Eval metric (Area Under the Curve – AUC) . Christoph Trattner 29.8.2014 – PUC, Chile
  36. 36. Social Computing @ Know-Center Results: Although the combination of features from both social and trading networks did not show a significant improvement over trading network data alone, our experiments indicate that the online social network data improve the predictive accuracy of trading interactions over random guessing by 28% in a cold-start setting. 36 Results: seller/buyer prediction Baseline: 0.5 (random guessing) Dataset: • 131,087 seller profiles with 268,852 trading interactions. • 169,035 social profiles with overall 3,175,304 social interactions. . Christoph Trattner 29.8.2014 – PUC, Chile
  37. 37. Social Computing @ Know-Center 37 Follow-up (1) Experiment with location-based social network data Task: Predict items to users User-based collaborative filtering . Christoph Trattner 29.8.2014 – PUC, Chile
  38. 38. Social Computing @ Know-Center 38 Follow-up (2) . Christoph Trattner 29.8.2014 – PUC, Chile
  39. 39. Social Computing @ Know-Center 39 Recsium Framework • Near Real-Time Updates • Real Time Recommendations • Deals with various sources of data • RESTful API . Christoph Trattner 29.8.2014 – PUC, Chile
  40. 40. Social Computing @ Know-Center 40 Demo - Recsium http://recsium.know-center.tugraz.at/recsium/ . Christoph Trattner 29.8.2014 – PUC, Chile
  41. 41. Social Computing @ Know-Center 41 ...currently working on Location-based services shopping malls, train-stations Technology: iBeacons Task: indoor navigation, indoor marketing, etc... . Christoph Trattner 29.8.2014 – PUC, Chile
  42. 42. Social Computing @ Know-Center 42 Project 3 University of Pittsburgh: Interested on the usefulness of Twitter in academic conferences. . Christoph Trattner 29.8.2014 – PUC, Chile
  43. 43. Social Computing @ Know-Center 43 Research Question 3: To what extent is Twitter useful to engage new comers (junior researchers) in academic conferences? Externals involved: • University of Pittsburgh, Pittsburgh, USA • PUC, Chile Wen,X., Parra, D. and Trattner, C.: How groups of people interact with each other on Twitter during academic conferences, In Proceedings of the 2014 ACM Conference on Computer Supported Cooperative Work (CSCW 2014), ACM, Baltimore, Maryland, USA. . Christoph Trattner 29.8.2014 – PUC, Chile
  44. 44. Social Computing @ Know-Center 44 Dataset § Data: We collected tweets data by searching for the hashtag of four conferences: Hypertext 2012 (#ht2012), UMAP 2012 (#umap2012), RecSys 2012 (#recsys2012), and ECTEL 2012 (#ectel2012). § Tweets Type: a) mentions, b) replies to, c) re-tweets, and d) isolated tweets (not a), b), c)) § Twitters Group: a) Junior researcher (JR), b) Senior researcher (SR), c) Faculty (F), d) Industry (I), and e) Organizations (OR). Dates captured # Users # Total tweets a) Mentions b) Replies c) RT . Christoph Trattner 29.8.2014 – PUC, Chile not a), b), c) % Users re-tweeted, mentioned , replied-to # F # I # JR # O # SR HT 12 June 24-28 61 254 24 19 105 106 34.40% 19 16 6 4 15 UMAP 12 July 16-20 51 234 32 16 104 82 37.30% 23 7 3 8 18 RECSYS 12 Sept. 10-13 266 2022 265 60 1087 610 34.60% 61 120 6 19 53 ECTEL 12 Sept. 18-21 91 434 17 138 38 241 46.20% 51 17 3 11 15
  45. 45. Social Computing @ Know-Center Results: Junior researchers show the lowest group attention, and conversation ration among all groups. 45 Who is receiving the attention? 9.00 8.00 7.00 6.00 5.00 4.00 3.00 2.00 1.00 0.00 Average Group Attention Per User Faculty Senior Researcher Junior Researcher Organization Industry HT 12 UMAP 12 RECSYS 12 ECTEL 12 16.00 14.00 12.00 10.00 8.00 6.00 4.00 2.00 0.00 Average Group Contribution Per User Faculty Senior Researcher Junior Researcher Organization Industry 0.90 0.80 0.70 0.60 0.50 0.40 0.30 0.20 0.10 Conversion Ratio . Christoph Trattner 29.8.2014 – PUC, Chile HT 12 UMAP 12 RECSYS 12 ECTEL 12 Conversion Ratio (CR) = Attention / Contribution = (|mentioned| + |replied| + |RT|) /|tweets| 0.00 Faculty Senior Researcher Junior Researcher Organization Industry HT 12 UMAP 12 RECSYS 12 ECTEL 12
  46. 46. Social Computing @ Know-Center Results: Juniors researchers are less involved in the conversation on Twitter than any other group of users. 46 Who interacts with whom? HT12 UMAP12 RECSYS12 ECTEL12 FromTo F SR JR O I F SR JR O I F SR JR O I F SR JR O I Faculty (F) 0.43 0.16 0.20 0.16 0.05 0.53 0.42 0.00 0.02 0.04 0.36 0.30 0.01 0.00 0.34 0.73 0.14 0.00 0.02 0.11 Senior Researcher (SR) 0.46 0.19 0.15 0.12 0.08 0.32 0.60 0.00 0.01 0.06 0.22 0.33 0.01 0.02 0.42 0.42 0.13 0.00 0.16 0.29 Junior Researcher (JR) 0.52 0.00 0.12 0.20 0.16 0.40 0.60 0.00 0.00 0.00 0.21 0.38 0.08 0.00 0.33 1.00 0.00 0.00 0.00 0.00 OrganizaTon (O) 0.26 0.30 0.15 0.26 0.04 0.50 0.40 0.00 0.10 0.00 0.15 0.26 0.02 0.08 0.49 0.20 0.20 0.00 0.27 0.33 Industry (I) 0.27 0.31 0.19 0.19 0.04 0.42 0.50 0.00 0.08 0.00 0.26 0.25 0.00 0.02 0.47 0.58 0.20 0.00 0.13 0.10 . Christoph Trattner 29.8.2014 – PUC, Chile
  47. 47. Social Computing @ Know-Center Results: Retweets and Mentions increase over time. Replies and Mentions stay steady over time. 47 Has usage changed over time? . Christoph Trattner 29.8.2014 – PUC, Chile
  48. 48. Social Computing @ Know-Center Results: Our analysis reveals a steady growth in the communication over twitter over time. Interestingly these conversations get less connected over time. 48 Has interaction changed over time? . Christoph Trattner 29.8.2014 – PUC, Chile
  49. 49. Social Computing @ Know-Center Results: Eigenvector centrality is the most important feature to predict future conference participation followed by degree centrality. 49 What keeps users returning over time? . Christoph Trattner 29.8.2014 – PUC, Chile
  50. 50. Social Computing @ Know-Center 50 ...ok that‘s basically it J . Christoph Trattner 29.8.2014 – PUC, Chile
  51. 51. Social Computing @ Know-Center 51 ...of course there are other projects . Christoph Trattner 29.8.2014 – PUC, Chile
  52. 52. Social Computing @ Know-Center 52 Thank you! Christoph Trattner Email: ctrattner@know-center.at Web: christophtrattner.info Twitter: @ctrattner Sponsors: . Christoph Trattner 29.8.2014 – PUC, Chile
  53. 53. Social Computing @ Know-Center 53 Any questions? . Christoph Trattner 29.8.2014 – PUC, Chile

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