KONECT Cloud – Large Scale Network Mining in the Cloud
•
0 likes•483 views
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.
Report
Share
Report
Share
Download to read offline
Recommended
Eight Formalisms for Defining Graph Models
We present eight different formalisms that can be used to define graph models.
Winning Science Slam by Jérôme Kunegis – First Prize at ICWSM 2016
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.
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.
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.
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.
How healthy is TYPO3?
Introducing the TYPO3 projects pear.typo3.org, ci.typo3.org and metrics.typo3.org
Talk about lessons we learned and what's the Roadmap for improving TYPO3 Core and TER quality.
Recommended
Eight Formalisms for Defining Graph Models
We present eight different formalisms that can be used to define graph models.
Winning Science Slam by Jérôme Kunegis – First Prize at ICWSM 2016
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.
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.
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.
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.
How healthy is TYPO3?
Introducing the TYPO3 projects pear.typo3.org, ci.typo3.org and metrics.typo3.org
Talk about lessons we learned and what's the Roadmap for improving TYPO3 Core and TER quality.
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.
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
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.
The Slashdot Zoo: Mining a Social Network with Negative Edges
We analyse the corpus of user relationships of the Slashdot technology news site. The data was collected from the Slashdot Zoo feature where users of the website can tag other users as friends and foes, providing positive and negative endorsements. We adapt social network analysis techniques to the problem of negative edge weights. In particular, we consider signed variants of global network characteristics such as
the clustering coefficient, node-level characteristics such as centrality and popularity measures, and link-level characteristics such as distances and similarity measures. We evaluate
these measures on the task of identifying unpopular users,
as well as on the task of predicting the sign of links and show that the network exhibits multiplicative transitivity which allows algebraic methods based on matrix multiplication to
be used. We compare our methods to traditional methods which are only suitable for positively weighted edges.
Spectral Analysis of Signed Graphs for Clustering, Prediction and Visualization
We study the application of spectral clustering, prediction and
visualization methods to graphs with negatively weighted edges. We show
that several characteristic matrices of graphs can be extended to graphs
with positively and negatively weighted edges, giving signed spectral
clustering methods, signed graph kernels and network visualization
methods that apply to signed graphs. In particular, we review a signed
variant of the graph Laplacian. We derive our results by considering
random walks, graph clustering, graph drawing and electrical networks,
showing that they all result in the same formalism for handling
negatively weighted edges. We illustrate our methods using examples
from social networks with negative edges and bipartite rating graphs.
Network Growth and the Spectral Evolution Model
We introduce and study the spectral evolution model, which characterizes
the growth of large networks in terms of the eigenvalue decomposition of
their adjacency matrices: In large networks, changes over time result in
a change of a graph's spectrum, leaving the eigenvectors unchanged. We
validate this hypothesis for several large social, collaboration,
authorship, rating, citation, communication and tagging networks,
covering unipartite, bipartite, signed and unsigned graphs. Following
these observations, we introduce a link prediction algorithm based on
the extrapolation of a network's spectral evolution. This new link
prediction method generalizes several common graph kernels that can be
expressed as spectral transformations. In contrast to these graph
kernels, the spectral extrapolation algorithm does not make assumptions
about specific growth patterns beyond the spectral evolution model. We
thus show that it performs particularly well for networks with
irregular, but spectral, growth patterns.
National Security Agency - NSA mobile device best practices
Threats to mobile devices are more prevalent and increasing in scope and complexity. Users of mobile devices desire to take full advantage of the features
available on those devices, but many of the features provide convenience and capability but sacrifice security. This best practices guide outlines steps the users can take to better protect personal devices and information.
GraphSummit Singapore | Enhancing Changi Airport Group's Passenger Experience...
Dr. Sean Tan, Head of Data Science, Changi Airport Group
Discover how Changi Airport Group (CAG) leverages graph technologies and generative AI to revolutionize their search capabilities. This session delves into the unique search needs of CAG’s diverse passengers and customers, showcasing how graph data structures enhance the accuracy and relevance of AI-generated search results, mitigating the risk of “hallucinations” and improving the overall customer journey.
More Related Content
More from Jérôme KUNEGIS
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.
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
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.
The Slashdot Zoo: Mining a Social Network with Negative Edges
We analyse the corpus of user relationships of the Slashdot technology news site. The data was collected from the Slashdot Zoo feature where users of the website can tag other users as friends and foes, providing positive and negative endorsements. We adapt social network analysis techniques to the problem of negative edge weights. In particular, we consider signed variants of global network characteristics such as
the clustering coefficient, node-level characteristics such as centrality and popularity measures, and link-level characteristics such as distances and similarity measures. We evaluate
these measures on the task of identifying unpopular users,
as well as on the task of predicting the sign of links and show that the network exhibits multiplicative transitivity which allows algebraic methods based on matrix multiplication to
be used. We compare our methods to traditional methods which are only suitable for positively weighted edges.
Spectral Analysis of Signed Graphs for Clustering, Prediction and Visualization
We study the application of spectral clustering, prediction and
visualization methods to graphs with negatively weighted edges. We show
that several characteristic matrices of graphs can be extended to graphs
with positively and negatively weighted edges, giving signed spectral
clustering methods, signed graph kernels and network visualization
methods that apply to signed graphs. In particular, we review a signed
variant of the graph Laplacian. We derive our results by considering
random walks, graph clustering, graph drawing and electrical networks,
showing that they all result in the same formalism for handling
negatively weighted edges. We illustrate our methods using examples
from social networks with negative edges and bipartite rating graphs.
Network Growth and the Spectral Evolution Model
We introduce and study the spectral evolution model, which characterizes
the growth of large networks in terms of the eigenvalue decomposition of
their adjacency matrices: In large networks, changes over time result in
a change of a graph's spectrum, leaving the eigenvectors unchanged. We
validate this hypothesis for several large social, collaboration,
authorship, rating, citation, communication and tagging networks,
covering unipartite, bipartite, signed and unsigned graphs. Following
these observations, we introduce a link prediction algorithm based on
the extrapolation of a network's spectral evolution. This new link
prediction method generalizes several common graph kernels that can be
expressed as spectral transformations. In contrast to these graph
kernels, the spectral extrapolation algorithm does not make assumptions
about specific growth patterns beyond the spectral evolution model. We
thus show that it performs particularly well for networks with
irregular, but spectral, growth patterns.
More from Jérôme KUNEGIS (19)
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
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
The Slashdot Zoo: Mining a Social Network with Negative Edges
The Slashdot Zoo: Mining a Social Network with Negative Edges
Spectral Analysis of Signed Graphs for Clustering, Prediction and Visualization
Spectral Analysis of Signed Graphs for Clustering, Prediction and Visualization
Recently uploaded
National Security Agency - NSA mobile device best practices
Threats to mobile devices are more prevalent and increasing in scope and complexity. Users of mobile devices desire to take full advantage of the features
available on those devices, but many of the features provide convenience and capability but sacrifice security. This best practices guide outlines steps the users can take to better protect personal devices and information.
GraphSummit Singapore | Enhancing Changi Airport Group's Passenger Experience...
Dr. Sean Tan, Head of Data Science, Changi Airport Group
Discover how Changi Airport Group (CAG) leverages graph technologies and generative AI to revolutionize their search capabilities. This session delves into the unique search needs of CAG’s diverse passengers and customers, showcasing how graph data structures enhance the accuracy and relevance of AI-generated search results, mitigating the risk of “hallucinations” and improving the overall customer journey.
UiPath Test Automation using UiPath Test Suite series, part 4
Welcome to UiPath Test Automation using UiPath Test Suite series part 4. In this session, we will cover Test Manager overview along with SAP heatmap.
The UiPath Test Manager overview with SAP heatmap webinar offers a concise yet comprehensive exploration of the role of a Test Manager within SAP environments, coupled with the utilization of heatmaps for effective testing strategies.
Participants will gain insights into the responsibilities, challenges, and best practices associated with test management in SAP projects. Additionally, the webinar delves into the significance of heatmaps as a visual aid for identifying testing priorities, areas of risk, and resource allocation within SAP landscapes. Through this session, attendees can expect to enhance their understanding of test management principles while learning practical approaches to optimize testing processes in SAP environments using heatmap visualization techniques
What will you get from this session?
1. Insights into SAP testing best practices
2. Heatmap utilization for testing
3. Optimization of testing processes
4. Demo
Topics covered:
Execution from the test manager
Orchestrator execution result
Defect reporting
SAP heatmap example with demo
Speaker:
Deepak Rai, Automation Practice Lead, Boundaryless Group and UiPath MVP
Climate Impact of Software Testing at Nordic Testing Days
My slides at Nordic Testing Days 6.6.2024
Climate impact / sustainability of software testing discussed on the talk. ICT and testing must carry their part of global responsibility to help with the climat warming. We can minimize the carbon footprint but we can also have a carbon handprint, a positive impact on the climate. Quality characteristics can be added with sustainability, and then measured continuously. Test environments can be used less, and in smaller scale and on demand. Test techniques can be used in optimizing or minimizing number of tests. Test automation can be used to speed up testing.
Pushing the limits of ePRTC: 100ns holdover for 100 days
At WSTS 2024, Alon Stern explored the topic of parametric holdover and explained how recent research findings can be implemented in real-world PNT networks to achieve 100 nanoseconds of accuracy for up to 100 days.
20240607 QFM018 Elixir Reading List May 2024
Everything I found interesting about the Elixir programming ecosystem in May 2024
Encryption in Microsoft 365 - ExpertsLive Netherlands 2024
In this session I delve into the encryption technology used in Microsoft 365 and Microsoft Purview. Including the concepts of Customer Key and Double Key Encryption.
Smart TV Buyer Insights Survey 2024 by 91mobiles.pdf
91mobiles recently conducted a Smart TV Buyer Insights Survey in which we asked over 3,000 respondents about the TV they own, aspects they look at on a new TV, and their TV buying preferences.
Transcript: Selling digital books in 2024: Insights from industry leaders - T...
The publishing industry has been selling digital audiobooks and ebooks for over a decade and has found its groove. What’s changed? What has stayed the same? Where do we go from here? Join a group of leading sales peers from across the industry for a conversation about the lessons learned since the popularization of digital books, best practices, digital book supply chain management, and more.
Link to video recording: https://bnctechforum.ca/sessions/selling-digital-books-in-2024-insights-from-industry-leaders/
Presented by BookNet Canada on May 28, 2024, with support from the Department of Canadian Heritage.
Microsoft - Power Platform_G.Aspiotis.pdf
Revolutionizing Application Development
with AI-powered low-code, presentation by George Aspiotis, Sr. Partner Development Manager, Microsoft
State of ICS and IoT Cyber Threat Landscape Report 2024 preview
The IoT and OT threat landscape report has been prepared by the Threat Research Team at Sectrio using data from Sectrio, cyber threat intelligence farming facilities spread across over 85 cities around the world. In addition, Sectrio also runs AI-based advanced threat and payload engagement facilities that serve as sinks to attract and engage sophisticated threat actors, and newer malware including new variants and latent threats that are at an earlier stage of development.
The latest edition of the OT/ICS and IoT security Threat Landscape Report 2024 also covers:
State of global ICS asset and network exposure
Sectoral targets and attacks as well as the cost of ransom
Global APT activity, AI usage, actor and tactic profiles, and implications
Rise in volumes of AI-powered cyberattacks
Major cyber events in 2024
Malware and malicious payload trends
Cyberattack types and targets
Vulnerability exploit attempts on CVEs
Attacks on counties – USA
Expansion of bot farms – how, where, and why
In-depth analysis of the cyber threat landscape across North America, South America, Europe, APAC, and the Middle East
Why are attacks on smart factories rising?
Cyber risk predictions
Axis of attacks – Europe
Systemic attacks in the Middle East
Download the full report from here:
https://sectrio.com/resources/ot-threat-landscape-reports/sectrio-releases-ot-ics-and-iot-security-threat-landscape-report-2024/
Alt. GDG Cloud Southlake #33: Boule & Rebala: Effective AppSec in SDLC using ...
Effective Application Security in Software Delivery lifecycle using Deployment Firewall and DBOM
The modern software delivery process (or the CI/CD process) includes many tools, distributed teams, open-source code, and cloud platforms. Constant focus on speed to release software to market, along with the traditional slow and manual security checks has caused gaps in continuous security as an important piece in the software supply chain. Today organizations feel more susceptible to external and internal cyber threats due to the vast attack surface in their applications supply chain and the lack of end-to-end governance and risk management.
The software team must secure its software delivery process to avoid vulnerability and security breaches. This needs to be achieved with existing tool chains and without extensive rework of the delivery processes. This talk will present strategies and techniques for providing visibility into the true risk of the existing vulnerabilities, preventing the introduction of security issues in the software, resolving vulnerabilities in production environments quickly, and capturing the deployment bill of materials (DBOM).
Speakers:
Bob Boule
Robert Boule is a technology enthusiast with PASSION for technology and making things work along with a knack for helping others understand how things work. He comes with around 20 years of solution engineering experience in application security, software continuous delivery, and SaaS platforms. He is known for his dynamic presentations in CI/CD and application security integrated in software delivery lifecycle.
Gopinath Rebala
Gopinath Rebala is the CTO of OpsMx, where he has overall responsibility for the machine learning and data processing architectures for Secure Software Delivery. Gopi also has a strong connection with our customers, leading design and architecture for strategic implementations. Gopi is a frequent speaker and well-known leader in continuous delivery and integrating security into software delivery.
Removing Uninteresting Bytes in Software Fuzzing
Imagine a world where software fuzzing, the process of mutating bytes in test seeds to uncover hidden and erroneous program behaviors, becomes faster and more effective. A lot depends on the initial seeds, which can significantly dictate the trajectory of a fuzzing campaign, particularly in terms of how long it takes to uncover interesting behaviour in your code. We introduce DIAR, a technique designed to speedup fuzzing campaigns by pinpointing and eliminating those uninteresting bytes in the seeds. Picture this: instead of wasting valuable resources on meaningless mutations in large, bloated seeds, DIAR removes the unnecessary bytes, streamlining the entire process.
In this work, we equipped AFL, a popular fuzzer, with DIAR and examined two critical Linux libraries -- Libxml's xmllint, a tool for parsing xml documents, and Binutil's readelf, an essential debugging and security analysis command-line tool used to display detailed information about ELF (Executable and Linkable Format). Our preliminary results show that AFL+DIAR does not only discover new paths more quickly but also achieves higher coverage overall. This work thus showcases how starting with lean and optimized seeds can lead to faster, more comprehensive fuzzing campaigns -- and DIAR helps you find such seeds.
- These are slides of the talk given at IEEE International Conference on Software Testing Verification and Validation Workshop, ICSTW 2022.
Communications Mining Series - Zero to Hero - Session 1
This session provides introduction to UiPath Communication Mining, importance and platform overview. You will acquire a good understand of the phases in Communication Mining as we go over the platform with you. Topics covered:
• Communication Mining Overview
• Why is it important?
• How can it help today’s business and the benefits
• Phases in Communication Mining
• Demo on Platform overview
• Q/A
Uni Systems Copilot event_05062024_C.Vlachos.pdf
Unlocking Productivity: Leveraging the Potential of Copilot in Microsoft 365, a presentation by Christoforos Vlachos, Senior Solutions Manager – Modern Workplace, Uni Systems
SAP Sapphire 2024 - ASUG301 building better apps with SAP Fiori.pdf
Building better applications for business users with SAP Fiori.
• What is SAP Fiori and why it matters to you
• How a better user experience drives measurable business benefits
• How to get started with SAP Fiori today
• How SAP Fiori elements accelerates application development
• How SAP Build Code includes SAP Fiori tools and other generative artificial intelligence capabilities
• How SAP Fiori paves the way for using AI in SAP apps
Recently uploaded (20)
FIDO Alliance Osaka Seminar: FIDO Security Aspects.pdf
FIDO Alliance Osaka Seminar: FIDO Security Aspects.pdf
National Security Agency - NSA mobile device best practices
National Security Agency - NSA mobile device best practices
GraphSummit Singapore | Enhancing Changi Airport Group's Passenger Experience...
GraphSummit Singapore | Enhancing Changi Airport Group's Passenger Experience...
UiPath Test Automation using UiPath Test Suite series, part 4
UiPath Test Automation using UiPath Test Suite series, part 4
Climate Impact of Software Testing at Nordic Testing Days
Climate Impact of Software Testing at Nordic Testing Days
Pushing the limits of ePRTC: 100ns holdover for 100 days
Pushing the limits of ePRTC: 100ns holdover for 100 days
Encryption in Microsoft 365 - ExpertsLive Netherlands 2024
Encryption in Microsoft 365 - ExpertsLive Netherlands 2024
Smart TV Buyer Insights Survey 2024 by 91mobiles.pdf
Smart TV Buyer Insights Survey 2024 by 91mobiles.pdf
Transcript: Selling digital books in 2024: Insights from industry leaders - T...
Transcript: Selling digital books in 2024: Insights from industry leaders - T...
State of ICS and IoT Cyber Threat Landscape Report 2024 preview
State of ICS and IoT Cyber Threat Landscape Report 2024 preview
Alt. GDG Cloud Southlake #33: Boule & Rebala: Effective AppSec in SDLC using ...
Alt. GDG Cloud Southlake #33: Boule & Rebala: Effective AppSec in SDLC using ...
Communications Mining Series - Zero to Hero - Session 1
Communications Mining Series - Zero to Hero - Session 1
Free Complete Python - A step towards Data Science
Free Complete Python - A step towards Data Science
SAP Sapphire 2024 - ASUG301 building better apps with SAP Fiori.pdf
SAP Sapphire 2024 - ASUG301 building better apps with SAP Fiori.pdf
KONECT Cloud – Large Scale Network Mining in the Cloud
- 1. KONECT Cloud Large Scale Network Mining in the Cloud Jérôme Kunegis Future SOC Lab Day, 18.04.2012 1
- 2. Networks are Everywhere ip r sh tho Au ip dsh Fr ien t Trus n tio n i ca mu e Co m re nc c n r c tio c cu ter a Co-o In
- 3. Social Networks friend
- 4. Trust Networks tru st
- 5. Friend/Enemy Network en d em en y fri
- 6. Interaction Network listen
- 7. KONECT – Koblenz Network Collection 148 network datasets 26 are undirected 38 are directed 84 are bipartite 59 have unweighted edges 77 allow multiple edges 04 have signed edges 08 have ratings as edges 78 have edge arrival times konect.uni-koblenz.de
- 8. Largest Network Directed “who follows who” network 0 041 652 230 users 1 468 365 182 edges konect.uni-koblenz.de/networks/twitter
- 9. 148 Network Datasets authorship communication co-occurrence features folksonomy interaction physical ratings reference semantic social trust
- 10. What We Computed Connected components Network diameter ← at Future SOC Lab Clustering coefficients Degree distributions Spectral distribution Eigenvector centrality Graph drawing Temporal Analysis Link prediction
- 12. 90 Percentile Effective Diameter 5
- 13. 90 Percentile Effective Diameter 3
- 14. 90 Percentile Effective Diameter 3.75
- 15. Computing the Effective Diameter for each node i { |V| count hops needed to reach 90% |E| } Total runtime: |E| × |V|
- 16. Graph Sampling Keep X% of edges
- 17. Computation × 1 000 vertices (sampled) × 120 840 391 edges × 20 sample sizes (5%, 10%, …, 100%) × 50 random samplings Evaluation on single machine: 1 TiB memory 64 cores Matlab 64 bit
- 18. Results
- 19. Thank You! Dr. Jérôme Kunegis konect.uni-koblenz.de kunegis@uni-koblenz.de west.uni-koblenz.de