Winning Science Slam by Jérôme Kunegis – First Prize at ICWSM 2016

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These are the slides of the winning Science Slam presentation by Jérôme Kunegis that won the First Prize at the Science Slam of the International Conference on Weblogs and Social Media (ICWSM) in 2016.

Internet

This Talk Is Not Scientific
● I hid an error somewhere on purpose.
To make you more attentive.

● I hid an error somewhere on purpose.
To make you more attentive.
● If you are allergic to hamsters, you may
want to put your fingers in your ears now.
This Talk Is Not Scientific

Your Hamster's Favourite Activity
113 Playing

Your Hamster's Favourite Activity
113 Playing
150 Chewing

Your Hamster's Favourite Activity
113 Playing
150 Chewing
161 Climbing

Your Hamster's Favourite Activity
113 Playing
150 Chewing
161 Climbing
224 Eating

Your Hamster's Favourite Activity
113 Playing
150 Chewing
161 Climbing
224 Eating
466 Sleeping

Your Hamster's Favourite Activity
113 Playing
150 Chewing
161 Climbing
224 Eating
466 Sleeping
835 Running

Bitches (* from Old English biċċe)
Bella Lucy Daisy

Tomcats (* Look it up)
Tigger Max Oliver

Fig. 1 What We Can
Learn From Hamsters.
Hamsters Don't Let the Internet
Genderize Them
Jérôme Kunegis¹ Party Hamster²
¹ University of Koblenz-Landau ² Royal Veterinary College London

More from Jérôme KUNEGIS

Talk by Jérôme Kunegis at BeNet'17 in Gent. In this talk, Jérôme Kunegis presented ongoing work, see the abstract below. Abstract: We describe a method for visually summarising the structure of large network datasets that works by drawing smaller graphs generated to have similar structural properties to the input graphs. Visualising complex networks is crucial to understand and make sense of networked data and the relationships it represents. Due to the large size of many networks, visualisation is extremely difficult; the simple method of \emph{drawing} large networks like those of Facebook or Twitter leads to graphics that convey little or no information. While modern graph layout algorithms can scale computationally to large networks, their output tends to a common \emph{hairball} look, which makes it difficult to even distinguish different graphs from each other. Graph sampling and graph coarsening techniques partially address these limitations but they are only able to preserve a subset of the properties of the original graphs. In this paper we take the problem of visualising large graphs from a novel perspective: we leave the original graph's nodes and edges behind, and instead summarise its properties such as the clustering coefficient and bipartivity by generating a completely new graph whose structural properties match that of the original graph. To verify the utility of this approach as compared to other graph visualisation algorithms, we perform an experimental evaluation in which we repeatedly asked experimental subjects (professionals in graph mining and related areas) to determine which of two given graphs has a given structural property and then assess which visualisation algorithm helped in identifying the correct answer. Our summarisation approach SynGraphy compares favourably to other techniques on a variety of networks.

Succinct Summarisation of Large Networks via Small Synthetic Representative G...

Jérôme KUNEGIS

Networks represent a type of dataset that is ubiquitous in many disciplines and areas. Examples are social networks (ties between people), communication networks, trophic networks ("who eats who"), the World Wide Web, computer networks, lexical networks (connections between words), transport networks, metabolic networks (e.g., interactions between proteins), neural networks, animal networks, citation networks, affiliation networks (of people in groups), software dependency networks, and many more. In this talk, we present ongoing work on answering the question "Can the type of network be detected from the network structure alone?" For instance, given a completely unlabeled network dataset consisting only of node and edges, can we detect whether the data represents a social network or a hyperlink network? We present machine learning and statistical approaches to answering questions of this type. The presented results will make use of data in the KONECT project, one of the largest repositories of network datasets, curated at the University of Namur.

Title: What Is the Difference between a Social and a Hyperlink Network? -- Ho...

Jérôme KUNEGIS

In this talk, Jérôme Kunegis will present recent results on the measurement of conflict in signed social network. A signed social network is a network in which both positive and negative ties are present, for instance representing friendship and enmity, or trust and distrust. Such networks have long been studied under the aspect of balance theory, i.e., considering whether the individuals can be grouped into two groups, such that the sign of all ties reflects the partition, or equivalently in terms of the individual configuration of tryads. The failure of such a structure to be present in a signed social network is usually designated as conflict, and measuring the amount of conflict in a given signed social network is an open problem. In this talk, a novel measure of conflict is presented, based on algebraic graph theory, and considering a previously known variant of the Laplacian matrix for signed graphs. The talk will show both theoretical motivations for the measure, as well as an evaluation of its utility at signed social network analysis using multiple real-world signed social networks, based on networks from the KONECT project. Jérôme Kunegis from the Center for Complex Networks (naXys) at the University of Namur (Belgium) is the founder and main lead of the KONECT project, a curated collection of network datasets, algorithms and analyses, maintained at the University of Namur.

Measuring the Conflict in a Social Network with Friends and Foes: A Recent Al...

Jérôme KUNEGIS

Schach und Computer

Jérôme KUNEGIS

In this talk, I present recent results on the measurement of conflict in signed social network. A signed social network is a network in which both positive and negative ties are present, for instance representing friendship and enmity, or trust and distrust. Such networks have long been studied under the aspect of balance theory, i.e., considering whether the individuals can be grouped into two groups, such that the sign of all ties reflects the partition, or equivalently in terms of the individual configuration of tryads. The failure of such a structure to be present in a signed social network is usually designated as conflict, and measuring the amount of conflict in a given signed social network is an open problem. In this work, I present a novel measure of conflict, based on algebraic graph theory, and considering a previously known variant of the Laplacian matrix for signed graphs. The talk will show both theoretical motivations for the measure, as well as an evaluation of its utility at signed social network analysis using multiple real-world signed social networks.

Algebraic Graph-theoretic Measures of Conflict

Jérôme KUNEGIS

Generating Networks with Arbitrary Properties

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Karriere Lounge – INFORMATIK 2013

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Eight Formalisms for Defining Graph Models

Jérôme KUNEGIS

We present the Koblenz Network Collection (KONECT), a project to collect network datasets in the areas of web science, network science and related areas, as well as provide tools for their analysis. In the cited areas, a surprisingly large number of very heterogeneous data can be modeled as networks and consequently, a unified representation of networks can be used to gain insight into many kinds of problems. Due to the emergence of the World Wide Web in the last decades many such datasets are now openly available. The KONECT project thus has the goal of collecting many diverse network datasets from the Web, and providing a way for their systematic study. The main parts of KONECT are (1)~a collection of over 160 network datasets, consisting of directed, undirected, unipartite, bipartite, weighted, unweighted, signed and temporal networks collected from the Web, (2)~a Matlab toolbox for network analysis and (3)~a website giving a compact overview the various computed statistics and plots. In this paper, we describe KONECT's taxonomy of networks datasets, give an overview of the datasets included, review the supported statistics and plots, and briefly discuss KONECT's role in the area of web science and network science.

KONECT – The Koblenz Network Collection

Jérôme KUNEGIS

We perform an empirical study of the preferential attachment phenomenon in temporal networks and show that on the Web, networks follow a nonlinear preferential attachment model in which the exponent depends on the type of network considered. The classical preferential attachment model for networks by Barabási and Albert (1999) assumes a linear relationship between the number of neighbors of a node in a network and the probability of attachment. Although this assumption is widely made in Web Science and related fields, the underlying linearity is rarely measured. To fill this gap, this paper performs an empirical longitudinal (time-based) study on forty-seven diverse Web network datasets from seven network categories and including directed, undirected and bipartite networks. We show that contrary to the usual assumption, preferential attachment is nonlinear in the networks under consideration. Furthermore, we observe that the deviation from linearity is dependent on the type of network, giving sublinear attachment in certain types of networks, and superlinear attachment in others. Thus, we introduce the preferential attachment exponent $\beta$ as a novel numerical network measure that can be used to discriminate different types of networks. We propose explanations for the behavior of that network measure, based on the mechanisms that underly the growth of the network in question.

Preferential Attachment in Online Networks: Measurement and Explanations

Jérôme KUNEGIS

Predicting Directed Links using Nondiagonal Matrix Decompositions

Jérôme KUNEGIS

A typical recommender setting is based on two kinds of relations: similarity between users (or between objects) and the taste of users towards certain objects. In environments such as online dating websites, these two relations are difficult to separate, as the users can be similar to each other, but also have preferences towards other users, i.e., rate other users. In this paper, we present a novel and unified way to model this duality of the relations by using split-complex numbers, a number system related to the complex numbers that is used in mathematics, physics and other fields. We show that this unified representation is capable of modeling both notions of relations between users in a joint expression and apply it for recommending potential partners. In experiments with the Czech dating website Libimseti.cz we show that our modeling approach leads to an improvement over baseline recommendation methods in this scenario.

Online Dating Recommender Systems: The Split-complex Number Approach

Jérôme KUNEGIS

Why Beyoncé Is More Popular Than Me – Fairness, Diversity and Other Measures

Jérôme KUNEGIS

Fairness on the Web: Alternatives to the Power Law (WebSci 2012)

Jérôme KUNEGIS

This paper presents several measures of fairness and inequality based on the degree distribution in networks, as alternatives to the well-established power-law exponent. Networks such as social networks, communication networks and the World Wide Web itself are often characterized by their unequal distribution of edges: Few nodes are attached to many edges, while many nodes are attached to only few edges. The inequality of such network structures is typically measured using the power-law exponent, stating that the number of nodes with a given degree is proportional to that degree taken to a certain exponent. However, this approach has several weaknesses, such as its narrow applicability and expensive computational complexity. Beyond the fact that power laws are by far not a universal phenomenon on the Web, the power-law exponent has the surprising property of being negatively correlated with the usual notion of inequality, making it unintuitive as a fairness measure. As alternatives, we propose several measures based on the Lorenz curve, which is used in economics but rarely in networks study, and on the information-theoretical concept of entropy. We show in experiments on a large collection of online networks that these measures do not suffer under the drawbacks of the power-law exponent.

Fairness on the Web: Alternatives to the Power Law

Jérôme KUNEGIS

In this thesis, I study the spectral characteristics of large dynamic networks and formulate the spectral evolution model. The spectral evolution model applies to networks that evolve over time, and describes their spectral decompositions such as the eigenvalue and singular value decomposition. The spectral evolution model states that over time, the eigenvalues of a network change while its eigenvectors stay approximately constant.

On the Spectral Evolution of Large Networks (PhD Thesis by Jérôme Kunegis)

Jérôme KUNEGIS

Searching Microblogs: Coping with Sparsity and Document Quality

Jérôme KUNEGIS

On the microblogging site Twitter, users can forward any message they receive to all of their followers. This is called a retweet and is usually done when users find a message particularly interesting and worth sharing with others. Thus, retweets reflect what the Twitter community considers interesting on a global scale, and can be used as a function of interestingness to generate a model to describe the content-based characteristics of retweets. In this paper, we analyze a set of high- and low-level content-based features on several large collections of Twitter messages. We train a prediction model to forecast for a given tweet its likelihood of being retweeted based on its contents. From the parameters learned by the model we deduce what are the influential content features that contribute to the likelihood of a retweet. As a result we obtain insights into what makes a message on Twitter worth retweeting and, thus, interesting.

Bad News Travel Fast: A Content-based Analysis of Interestingness on Twitter

Jérôme KUNEGIS

We study the scalability of several recent graph kernel-based collaborative recommendation algorithms. We compare the performance of several graph kernel-based recommendation algorithms, focusing on runtime and recommendation accuracy with respect to the reduced rank of the subspace. We inspect the exponential and Laplacian exponential kernels, the resistance distance kernel, the regularized Laplacian kernel, and the stochastic diffusion kernel. Furthermore, we introduce new variants of kernels based on the graph Laplacian which, in contrast to existing kernels, also allow negative edge weights and thus negative ratings. We perform an evaluation on the Netﬂix Prize rating corpus on prediction and recommendation tasks, showing that dimensionality reduction not only makes prediction faster, but sometimes also more accurate.

On the Scalability of Graph Kernels Applied to Collaborative Recommenders

Jérôme KUNEGIS

We present a unified framework for learning link prediction and edge weight prediction functions in large networks, based on the transformation of a graph’s algebraic spectrum. Our approach generalizes several graph kernels and dimensionality reduction methods and provides a method to estimate their parameters eﬃciently. We show how the parameters of these prediction functions can be learned by reducing the problem to a one-dimensional regression problem whose runtime only depends on the method’s reduced rank and that can be inspected visually. We derive variants that apply to undirected, weighted, unweighted, unipartite and bipartite graphs. We evaluate our method experimentally using examples from social networks, collaborative filtering, trust networks, citation networks, authorship graphs and hyperlink networks.

Learning Spectral Graph Transformations for Link Prediction

Jérôme KUNEGIS

More from Jérôme KUNEGIS (20)

Succinct Summarisation of Large Networks via Small Synthetic Representative G...

Title: What Is the Difference between a Social and a Hyperlink Network? -- Ho...

Measuring the Conflict in a Social Network with Friends and Foes: A Recent Al...

Schach und Computer

Algebraic Graph-theoretic Measures of Conflict

Generating Networks with Arbitrary Properties

Karriere Lounge – INFORMATIK 2013

Eight Formalisms for Defining Graph Models

KONECT – The Koblenz Network Collection

Preferential Attachment in Online Networks: Measurement and Explanations

Predicting Directed Links using Nondiagonal Matrix Decompositions

Online Dating Recommender Systems: The Split-complex Number Approach

Why Beyoncé Is More Popular Than Me – Fairness, Diversity and Other Measures

Fairness on the Web: Alternatives to the Power Law (WebSci 2012)

Fairness on the Web: Alternatives to the Power Law

On the Spectral Evolution of Large Networks (PhD Thesis by Jérôme Kunegis)

Searching Microblogs: Coping with Sparsity and Document Quality

Bad News Travel Fast: A Content-based Analysis of Interestingness on Twitter

On the Scalability of Graph Kernels Applied to Collaborative Recommenders

Learning Spectral Graph Transformations for Link Prediction

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2nd Solid Symposium: Solid Pods vs Personal Knowledge Graphs

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学校颁发一模一样【微信：95270640 】【(Curtin毕业证书)科廷大学毕业证成绩单】【微信：95270640 】学位证，留信认证（真实可查，永久存档）原件一模一样/offer、外壳等材料/诚信可靠,可直接看成品样本，帮您解决无法毕业带来的各种难题！原版制作，诚信可靠，可直接看成品样本。行业标杆！精益求精，诚心合作，真诚制作！多年品质 ,按需精细制作，24小时接单,全套进口原装设备。十五年致力于帮助留学生解决难题，包您满意。本公司拥有海外各大学样板无数，能完美还原。 1:1完美还原海外各大学毕业材料上的工艺：水印，阴影底纹，钢印LOGO烫金烫银，LOGO烫金烫银复合重叠。文字图案浮雕、激光镭射、紫外荧光、温感、复印防伪等防伪工艺。材料咨询办理、认证咨询办理请加学历顾问微信：95270640 【主营项目】一.毕业证【微信：95270640】成绩单、使馆认证、教育部认证、学生卡等！二.真实使馆公证(即留学回国人员证明,不成功不收费) 三.真实教育部学历学位认证（教育部存档！教育部留服网站永久可查）四.办理各国各大学文凭(一对一专业服务,可全程监控跟踪进度) 如果您处于以下几种情况： ◇在校期间，因各种原因未能顺利毕业……拿不到官方毕业证【微信：95270640】 ◇面对父母的压力，希望尽快拿到； ◇不清楚认证流程以及材料该如何准备； ◇回国时间很长，忘记办理； ◇回国马上就要找工作，办给用人单位看； ◇企事业单位必须要求办理的 ◇需要报考公务员、购买免税车、落转户口 ◇申请留学生创业基金留信网认证的作用: 1:该专业认证可证明留学生真实身份 2:同时对留学生所学专业登记给予评定 3:国家专业人才认证中心颁发入库证书 4:这个认证书并且可以归档倒地方 5:凡事获得留信网入网的信息将会逐步更新到个人身份内，将在公安局网内查询个人身份证信息后，同步读取人才网入库信息 6:个人职称评审加20分 7:个人信誉贷款加10分 8:在国家人才网主办的国家网络招聘大会中纳入资料，供国家高端企业选择人才办理Curtin毕业证书)科廷大学【微信：95270640】外观非常简单，由纸质材料制成，上面印有校徽、校名、毕业生姓名、专业等信息。办理Curtin毕业证书)科廷大学【微信：95270640】格式相对统一，各专业都有相应的模板。通常包括以下部分：校徽：象征着学校的荣誉和传承。校名:学校英文全称授予学位：本部分将注明获得的具体学位名称。毕业生姓名：这是最重要的信息之一，标志着该证书是由特定人员获得的。颁发日期：这是毕业正式生效的时间，也代表着毕业生学业的结束。其他信息：根据不同的专业和学位，可能会有一些特定的信息或章节。办理Curtin毕业证书)科廷大学毕业证价值很高，需要妥善保管。一般来说，应放置在安全、干燥、防潮的地方，避免长时间暴露在阳光下。如需使用，最好使用复印件而不是原件，以免丢失。综上所述，办理Curtin毕业证书)科廷大学毕业证是证明身份和学历的高价值文件。外观简单庄重，格式统一，包括重要的个人信息和发布日期。对持有人来说，妥善保管是非常重要的。

一比一原版(Curtin毕业证书)科廷大学毕业证原件一模一样

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原版制作美国爱荷华大学毕业证（iowa毕业证书）学位证网上存档可查【Q/微信741003700】原版仿制全套留学文凭材料（毕业证/成绩单（GPA成绩修改）/文凭学历证书/在读证明、毕业完成信、Offer录取通知书)；（真实可查）教育部学历认证、留信网认证、文凭认证、diploma、certificate、Degree、Transcript（实体公司，专业可靠）。 1:1完美还原海外各大学证书上的工艺：水印，阴影底纹，钢印LOGO烫金烫银，LOGO烫金烫银复合重叠。文字图案浮雕、激光镭射、紫外荧光、温感、复印防伪等防伪工艺。材料咨询办理、认证咨询办理请加学历顾问Q/微741003700。留学生归国服务中心面向美国英国澳洲加拿大留学生提供以下服务: 一、办理毕业证成绩单（学校原版1：1高仿真制作）二、留信认证（留学回国人员学历认证，网上存档可查，查到后付款）三、教育部学历认证（中国教育部留服中心存档可查,查到后付款）办理流程 1、收集客户办理信息； 2、客户付定金下单、公司确认到账转制作点做电子版； 3、电子版做好发给客户确认、电子版确认好转成品部做成品； 4、成品做好拍照或者视频确认再付余款； 5、快递给客户（国内顺丰，国外DHL）。本公司诚聘美国、加拿大、英国、新西兰、澳洲、法国、德国、新加坡各地代理人员，如果你有业余时间有兴趣就请联系我们校园代理，报酬丰厚。真诚期待您的加盟。请联系本公司学历认证顾问(QQ：741003700 微信：741003700)欢迎咨询！最专业的学历顾问，最有经验的顶尖OP为广大留学生的归国之路保卫护航！

原版制作美国爱荷华大学毕业证（iowa毕业证书）学位证网上存档可查

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2nd Solid Symposium: Solid Pods vs Personal Knowledge Graphs

一比一原版(Curtin毕业证书)科廷大学毕业证原件一模一样

原版制作美国爱荷华大学毕业证（iowa毕业证书）学位证网上存档可查

Winning Science Slam by Jérôme Kunegis – First Prize at ICWSM 2016

1. Thank You!

2. This Talk Is Not Scientific

3. This Talk Is Not Scientific ● I hid an error somewhere on purpose. To make you more attentive.

4. ● I hid an error somewhere on purpose. To make you more attentive. ● If you are allergic to hamsters, you may want to put your fingers in your ears now. This Talk Is Not Scientific

10.

11.

12.

13.

14.

15. Your Hamster's Favourite Activity

16. Your Hamster's Favourite Activity 113 Playing

17. Your Hamster's Favourite Activity 113 Playing 150 Chewing

18. Your Hamster's Favourite Activity 113 Playing 150 Chewing 161 Climbing

19. Your Hamster's Favourite Activity 113 Playing 150 Chewing 161 Climbing 224 Eating

20. Your Hamster's Favourite Activity 113 Playing 150 Chewing 161 Climbing 224 Eating 466 Sleeping

21. Your Hamster's Favourite Activity 113 Playing 150 Chewing 161 Climbing 224 Eating 466 Sleeping 835 Running

22. The Internet's Favourite Pet Names

23. Male Dogs

24. Male Dogs Max Buddy Jake

25. Bitches (* from Old English biċċe)

26. Bitches (* from Old English biċċe) Bella Lucy Daisy

27. Tomcats (* Look it up)

28. Tomcats (* Look it up) Tigger Max Oliver

29. Mollies (yes)

30. Mollies (yes) Chloe Bella Lucy

31. Male Hamsters Female Hamsters

32. Male Hamsters Female Hamsters

33. Fig. 1 What We Can Learn From Hamsters. Hamsters Don't Let the Internet Genderize Them Jérôme Kunegis¹ Party Hamster² ¹ University of Koblenz-Landau ² Royal Veterinary College London

Winning Science Slam by Jérôme Kunegis – First Prize at ICWSM 2016

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Winning Science Slam by Jérôme Kunegis – First Prize at ICWSM 2016