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.
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KONECT Cloud – Large Scale Network Mining in the Cloud
In the Winter 2011/2012 run at the Future SOC Lab, we used the KONECT
framework (Koblenz Network Collection) to compute ten
different network statistics on a large collection of downsampled
versions of a large network dataset, with the goal of determining
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computational effort needed to compute a network statistic. Preliminary
results show that this is indeed the case.
What Is the Added Value of Negative Links in Online Social Networks?
We investigate the "negative link" feature of social networks that
allows users to tag other users as foes or as distrusted
in addition to the usual friend and trusted links. To
answer the question whether negative links have an added value for an
online social network, we investigate the machine learning problem of
predicting the negative links of such a network using only the positive
links as a basis, with the idea that if this problem can be solved with
high accuracy, then the "negative link" feature is redundant. In
doing so, we also present a general methodology for assessing the added
value of any new link type in online social networks. Our evaluation is
performed on two social networks that allow negative links: The
technology news website Slashdot and the product review site Epinions.
In experiments with these two datasets, we come to the conclusion that a
combination of centrality-based and proximity-based link prediction
functions can be used to predict the negative edges in the networks we
analyse. We explain this result by an application of the models of
preferential attachment and balance theory to our learning problem, and
show that the "negative link" feature has a small but measurable added
value for these social networks.
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This is a presentaion of the most grammatical errors that learners of English make whilst learning grammar.
KONECT Cloud – Large Scale Network Mining in the Cloud
In the Winter 2011/2012 run at the Future SOC Lab, we used the KONECT
framework (Koblenz Network Collection) to compute ten
different network statistics on a large collection of downsampled
versions of a large network dataset, with the goal of determining
whether sampling of a large network can be used to reduce the
computational effort needed to compute a network statistic. Preliminary
results show that this is indeed the case.
What Is the Added Value of Negative Links in Online Social Networks?
We investigate the "negative link" feature of social networks that
allows users to tag other users as foes or as distrusted
in addition to the usual friend and trusted links. To
answer the question whether negative links have an added value for an
online social network, we investigate the machine learning problem of
predicting the negative links of such a network using only the positive
links as a basis, with the idea that if this problem can be solved with
high accuracy, then the "negative link" feature is redundant. In
doing so, we also present a general methodology for assessing the added
value of any new link type in online social networks. Our evaluation is
performed on two social networks that allow negative links: The
technology news website Slashdot and the product review site Epinions.
In experiments with these two datasets, we come to the conclusion that a
combination of centrality-based and proximity-based link prediction
functions can be used to predict the negative edges in the networks we
analyse. We explain this result by an application of the models of
preferential attachment and balance theory to our learning problem, and
show that the "negative link" feature has a small but measurable added
value for these social networks.
Succinct Summarisation of Large Networks via Small Synthetic Representative G...
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.
Title: What Is the Difference between a Social and a Hyperlink Network? -- Ho...
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.
Measuring the Conflict in a Social Network with Friends and Foes: A Recent Al...
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.
Schach und Computer
A talk given in 2006 by Jérôme Kunegis about computer chess: from the beginning to the present, culminating with the match Kasparov – Deep Blue. Given in German at a seminar organised at the Technical University of Berlin Horst Zuse, computer scientist and son of computer pioneer Konrad Zuse.
Algebraic Graph-theoretic Measures of Conflict
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.
Karriere Lounge – INFORMATIK 2013
Publicity slides for the "Karriere-Lounge – INFORMATIK" event (in German), a job fair held during the INFORMATIK 2013 conference.
http://www.informatik2013.de/karrierelounge_de.html
Eight Formalisms for Defining Graph Models
We present eight different formalisms that can be used to define graph models.
KONECT – The Koblenz Network Collection
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.
Preferential Attachment in Online Networks: Measurement and Explanations
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.
Online Dating Recommender Systems: The Split-complex Number Approach
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.
Fairness on the Web: Alternatives to the Power Law
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.
On the Spectral Evolution of Large Networks (PhD Thesis by 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.
Bad News Travel Fast: A Content-based Analysis of Interestingness on Twitter
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.
On the Scalability of Graph Kernels Applied to Collaborative Recommenders
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.
Learning Spectral Graph Transformations for Link Prediction
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.
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Succinct Summarisation of Large Networks via Small Synthetic Representative G...
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.
Title: What Is the Difference between a Social and a Hyperlink Network? -- Ho...
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.
Measuring the Conflict in a Social Network with Friends and Foes: A Recent Al...
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.
Schach und Computer
A talk given in 2006 by Jérôme Kunegis about computer chess: from the beginning to the present, culminating with the match Kasparov – Deep Blue. Given in German at a seminar organised at the Technical University of Berlin Horst Zuse, computer scientist and son of computer pioneer Konrad Zuse.
Algebraic Graph-theoretic Measures of Conflict
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.
Karriere Lounge – INFORMATIK 2013
Publicity slides for the "Karriere-Lounge – INFORMATIK" event (in German), a job fair held during the INFORMATIK 2013 conference.
http://www.informatik2013.de/karrierelounge_de.html
Eight Formalisms for Defining Graph Models
We present eight different formalisms that can be used to define graph models.
KONECT – The Koblenz Network Collection
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.
Preferential Attachment in Online Networks: Measurement and Explanations
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.
Online Dating Recommender Systems: The Split-complex Number Approach
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.
Fairness on the Web: Alternatives to the Power Law
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.
On the Spectral Evolution of Large Networks (PhD Thesis by 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.
Bad News Travel Fast: A Content-based Analysis of Interestingness on Twitter
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.
On the Scalability of Graph Kernels Applied to Collaborative Recommenders
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.
Learning Spectral Graph Transformations for Link Prediction
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.
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Succinct Summarisation of Large Networks via Small Synthetic Representative G...
Succinct Summarisation of Large Networks via Small Synthetic Representative G...
Title: What Is the Difference between a Social and a Hyperlink Network? -- Ho...
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...
Measuring the Conflict in a Social Network with Friends and Foes: A Recent Al...
Preferential Attachment in Online Networks: Measurement and Explanations
Preferential Attachment in Online Networks: Measurement and Explanations
Predicting Directed Links using Nondiagonal Matrix Decompositions
Predicting Directed Links using Nondiagonal Matrix Decompositions
Online Dating Recommender Systems: The Split-complex Number Approach
Online Dating Recommender Systems: The Split-complex Number Approach
Why Beyoncé Is More Popular Than Me – Fairness, Diversity and Other Measures
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 (WebSci 2012)
Fairness on the Web: Alternatives to the Power Law
Fairness on the Web: Alternatives to the Power Law
On the Spectral Evolution of Large Networks (PhD Thesis by Jérôme Kunegis)
On the Spectral Evolution of Large Networks (PhD Thesis by Jérôme Kunegis)
Searching Microblogs: Coping with Sparsity and Document Quality
Searching Microblogs: Coping with Sparsity and Document Quality
Bad News Travel Fast: A Content-based Analysis of Interestingness on Twitter
Bad News Travel Fast: A Content-based Analysis of Interestingness on Twitter
On the Scalability of Graph Kernels Applied to Collaborative Recommenders
On the Scalability of Graph Kernels Applied to Collaborative Recommenders
Learning Spectral Graph Transformations for Link Prediction
Learning Spectral Graph Transformations for Link Prediction
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假文凭国外(Adelaide毕业证)澳大利亚国立大学毕业证成绩单办理
购买澳大利亚国立大学毕业证>【微信176555708】办理澳大利亚国立大学毕业证成绩单【微信176555708】Adelaide毕业证成绩单Adelaide学历证书Adelaide文凭【Adelaide毕业套号文凭网认证澳大利亚国立大学毕业证成绩单】【哪里买澳大利亚国立大学毕业证文凭Adelaide成绩学校快递邮寄信封】【开版澳大利亚国立大学文凭】Adelaide留信认证本科硕士学历认证(诚招代理)微信：176555708办理国外高校毕业证成绩单文凭学位证,真实使馆公证（留学回国人员证明）真实留信网认证国外学历学位认证雅思代考国外学校代申请名校保录开请假条改GPA改成绩ID卡
1.高仿业务:【本科硕士】毕业证,成绩单（GPA修改）,学历认证（教育部认证）,大学Offer,,ID,留信认证,使馆认证,雅思,语言证书等高仿类证书；
2.认证服务: 学历认证（教育部认证）,大使馆认证（回国人员证明）,留信认证（可查有编号证书）,大学保录取,雅思保分成绩单。
3.技术服务：钢印水印烫金激光防伪凹凸版设计印刷激凸温感光标底纹镭射速度快。
办理澳大利亚国立大学澳大利亚国立大学毕业证成绩单流程：
1客户提供办理信息：姓名生日专业学位毕业时间等（如信息不确定可以咨询顾问：我们有专业老师帮你查询）；
2开始安排制作毕业证成绩单电子图；
3毕业证成绩单电子版做好以后发送给您确认；
4毕业证成绩单电子版您确认信息无误之后安排制作成品；
5成品做好拍照或者视频给您确认；
6快递给客户（国内顺丰国外DHLUPS等快读邮寄）
-办理真实使馆公证（即留学回国人员证明）
-办理各国各大学文凭（世界名校一对一专业服务,可全程监控跟踪进度）
-全套服务：毕业证成绩单真实使馆公证真实教育部认证。让您回国发展信心十足！
（详情请加一下 文凭顾问+微信:176555708）欢迎咨询！
一比一原版(LBS毕业证)伦敦商学院毕业证成绩单专业办理
LBS毕业证原版定制【微信：176555708】【伦敦商学院毕业证成绩单-学位证】【微信：176555708】（留信学历认证永久存档查询）采用学校原版纸张、特殊工艺完全按照原版一比一制作（包括：隐形水印，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）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，业务范围有加拿大、英国、澳洲、韩国、美国、新加坡，新西兰等学历材料，包您满意。
◆◆◆◆◆ — — — — — — — — 【留学教育】留学归国服务中心 — — — — — -◆◆◆◆◆
【主营项目】
一.毕业证【微信：176555708】成绩单、使馆认证、教育部认证、雅思托福成绩单、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：176555708】
◇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→ 【关于价格问题（保证一手价格）
我们所定的价格是非常合理的，而且我们现在做得单子大多数都是代理和回头客户介绍的所以一般现在有新的单子 我给客户的都是第一手的代理价格，因为我想坦诚对待大家 不想跟大家在价格方面浪费时间
对于老客户或者被老客户介绍过来的朋友，我们都会适当给一些优惠。
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
选择实体注册公司办理，更放心，更安全！我们的承诺：可来公司面谈，可签订合同，会陪同客户一起到教育部认证窗口递交认证材料，客户在教育部官方认证查询网站查询到认证通过结果后付款，不成功不收费！
学历顾问：微信：176555708
Gen Z and the marketplaces - let's translate their needs
The product workshop focused on exploring the requirements of Generation Z in relation to marketplace dynamics. We delved into their specific needs, examined the specifics in their shopping preferences, and analyzed their preferred methods for accessing information and making purchases within a marketplace. Through the study of real-life cases , we tried to gain valuable insights into enhancing the marketplace experience for Generation Z.
The workshop was held on the DMA Conference in Vienna June 2024.
Italy Agriculture Equipment Market Outlook to 2027
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国外证书(Lincoln毕业证)新西兰林肯大学毕业证成绩单不能毕业办理
退学办理新西兰林肯大学毕业证【微信176555708】学历认证怎么做：原版仿制新西兰林肯大学电子版成绩单毕业证认证【新西兰林肯大学毕业证成绩单】、新西兰林肯大学文凭证书成绩单复刻offer录取通知书、购买Lincoln圣力嘉学院本科毕业证、【新西兰林肯大学毕业证办理Lincoln毕业证书哪里买】、新西兰林肯大学 Offer在线办理Lincoln Offer新西兰林肯大学Bachloer Degree。1客户提供办理信息：姓名生日专业学位毕业时间等（如信息不确定可以咨询顾问：微信176555708我们有专业老师帮你查询）；
2开始安排制作毕业证成绩单电子图；
3毕业证成绩单电子版做好以后发送给您确认；
4毕业证成绩单电子版您确认信息无误之后安排制作成品；
5成品做好拍照或者视频给您确认；
6快递给客户（国内顺丰国外DHLUPS等快读邮寄）。
7完成交易删除客户资料
高精端提供以下服务：
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二：真实使馆认证（留学人员回国证明）使馆存档
三：真实教育部认证教育部存档教育部留服网站可查
四：留信认证留学生信息网站可查
五：与学校颁发的相关证件1:1纸质尺寸制定（定期向各大院校毕业生购买最新版本毕,业证成绩单保证您拿到的是鲁昂大学内部最新版本毕业证成绩单微信176555708）
A.为什么留学生需要操作留信认证?
留信认证全称全国留学生信息服务网认证,隶属于北京中科院。①留信认证门槛条件更低,费用更美丽,并且包过,完单周期短,效率高②留信认证虽然不能去国企,但是一般的公司都没有问题,因为国内很多公司连基本的留学生学历认证都不了解。这对于留学生来说,这就比自己光拿一个证书更有说服力,因为留学学历可以在留信网站上进行查询!
B.为什么我们提供的毕业证成绩单具有使用价值？
查询留服认证是国内鉴别留学生海外学历的唯一途径但认证只是个体行为不是所有留学生都操作所以没有办理认证的留学生的学历在国内也是查询不到的他们也仅仅只有一张文凭。所以这时候我们提供的和学校颁发的一模一样的毕业证成绩单就有了使用价值。
Meet up Milano 14 _ Axpo Italia_ Migration from Mule3 (On-prem) to.pdf
Quattordicesimo Meetup di Milano, tenutosi a Milano il 23 Maggio 2024 dalle ore 17:00 alle ore 18:30 in presenza e da remoto.
Abbiamo parlato di come Axpo Italia S.p.A. ha ridotto il technical debt migrando le proprie APIs da Mule 3.9 a Mule 4.4 passando anche da on-premises a CloudHub 1.0.
APNIC Foundation, presented by Ellisha Heppner at the PNG DNS Forum 2024
Ellisha Heppner, Grant Management Lead, presented an update on APNIC Foundation to the PNG DNS Forum held from 6 to 10 May, 2024 in Port Moresby, Papua New Guinea.
急速办(bedfordhire毕业证书)英国贝德福特大学毕业证成绩单原版一模一样
原版纸张【微信：741003700 】【(bedfordhire毕业证书)英国贝德福特大学毕业证成绩单】【微信：741003700 】学位证，留信认证（真实可查，永久存档）offer、雅思、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！外壳，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，能完美还原海外各大学 Bachelor Diploma degree, Master Degree Diploma
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Q/微741003700
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
一比一原版(CSU毕业证)加利福尼亚州立大学毕业证成绩单专业办理
CSU毕业证原版定制【微信：176555708】【加利福尼亚州立大学毕业证成绩单-学位证】【微信：176555708】（留信学历认证永久存档查询）采用学校原版纸张、特殊工艺完全按照原版一比一制作（包括：隐形水印，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）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，业务范围有加拿大、英国、澳洲、韩国、美国、新加坡，新西兰等学历材料，包您满意。
◆◆◆◆◆ — — — — — — — — 【留学教育】留学归国服务中心 — — — — — -◆◆◆◆◆
【主营项目】
一.毕业证【微信：176555708】成绩单、使馆认证、教育部认证、雅思托福成绩单、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：176555708】
◇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→ 【关于价格问题（保证一手价格）
我们所定的价格是非常合理的，而且我们现在做得单子大多数都是代理和回头客户介绍的所以一般现在有新的单子 我给客户的都是第一手的代理价格，因为我想坦诚对待大家 不想跟大家在价格方面浪费时间
对于老客户或者被老客户介绍过来的朋友，我们都会适当给一些优惠。
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
选择实体注册公司办理，更放心，更安全！我们的承诺：可来公司面谈，可签订合同，会陪同客户一起到教育部认证窗口递交认证材料，客户在教育部官方认证查询网站查询到认证通过结果后付款，不成功不收费！
学历顾问：微信：176555708
Understanding User Behavior with Google Analytics.pdf
Unlocking the full potential of Google Analytics is crucial for understanding and optimizing your website’s performance. This guide dives deep into the essential aspects of Google Analytics, from analyzing traffic sources to understanding user demographics and tracking user engagement.
Traffic Sources Analysis:
Discover where your website traffic originates. By examining the Acquisition section, you can identify whether visitors come from organic search, paid campaigns, direct visits, social media, or referral links. This knowledge helps in refining marketing strategies and optimizing resource allocation.
User Demographics Insights:
Gain a comprehensive view of your audience by exploring demographic data in the Audience section. Understand age, gender, and interests to tailor your marketing strategies effectively. Leverage this information to create personalized content and improve user engagement and conversion rates.
Tracking User Engagement:
Learn how to measure user interaction with your site through key metrics like bounce rate, average session duration, and pages per session. Enhance user experience by analyzing engagement metrics and implementing strategies to keep visitors engaged.
Conversion Rate Optimization:
Understand the importance of conversion rates and how to track them using Google Analytics. Set up Goals, analyze conversion funnels, segment your audience, and employ A/B testing to optimize your website for higher conversions. Utilize ecommerce tracking and multi-channel funnels for a detailed view of your sales performance and marketing channel contributions.
Custom Reports and Dashboards:
Create custom reports and dashboards to visualize and interpret data relevant to your business goals. Use advanced filters, segments, and visualization options to gain deeper insights. Incorporate custom dimensions and metrics for tailored data analysis. Integrate external data sources to enrich your analytics and make well-informed decisions.
This guide is designed to help you harness the power of Google Analytics for making data-driven decisions that enhance website performance and achieve your digital marketing objectives. Whether you are looking to improve SEO, refine your social media strategy, or boost conversion rates, understanding and utilizing Google Analytics is essential for your success.
一比一原版(SLU毕业证)圣路易斯大学毕业证成绩单专业办理
SLU毕业证原版定制【微信：176555708】【圣路易斯大学毕业证成绩单-学位证】【微信：176555708】（留信学历认证永久存档查询）采用学校原版纸张、特殊工艺完全按照原版一比一制作（包括：隐形水印，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）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，业务范围有加拿大、英国、澳洲、韩国、美国、新加坡，新西兰等学历材料，包您满意。
◆◆◆◆◆ — — — — — — — — 【留学教育】留学归国服务中心 — — — — — -◆◆◆◆◆
【主营项目】
一.毕业证【微信：176555708】成绩单、使馆认证、教育部认证、雅思托福成绩单、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：176555708】
◇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→ 【关于价格问题（保证一手价格）
我们所定的价格是非常合理的，而且我们现在做得单子大多数都是代理和回头客户介绍的所以一般现在有新的单子 我给客户的都是第一手的代理价格，因为我想坦诚对待大家 不想跟大家在价格方面浪费时间
对于老客户或者被老客户介绍过来的朋友，我们都会适当给一些优惠。
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
选择实体注册公司办理，更放心，更安全！我们的承诺：可来公司面谈，可签订合同，会陪同客户一起到教育部认证窗口递交认证材料，客户在教育部官方认证查询网站查询到认证通过结果后付款，不成功不收费！
学历顾问：微信：176555708
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- 1. Thank You!
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