@inproceedings{christophscholzandmartinatzmuellerandalainbarratandcirocattutoandgerdstumme2013insights, author = {Christoph Scholz and Martin Atzmueller and Alain Barrat and Ciro Cattuto and Gerd Stumme}, title = {New Insights and Methods For Predicting Face-To-Face Contacts}, booktitle = {Proc. 7th Intl. AAAI Conference on Weblogs and Social Media}, publisher = {AAAI Press}, address = {Palo Alto, CA, USA}, year = {2013}, keywords = {2013, conferator, contact, face-to-face, iteg, itegpub, l3s, link, myown, networks, prediction, venus} }

%0 = inproceedings %A = Christoph Scholz and Martin Atzmueller and Alain Barrat and Ciro Cattuto and Gerd Stumme %B = Proc. 7th Intl. AAAI Conference on Weblogs and Social Media %C = Palo Alto, CA, USA %D = 2013 %I = AAAI Press %T = New Insights and Methods For Predicting Face-To-Face Contacts

With social media and the according social and ubiquitous applications
nding their way into everyday life, there is a rapidly growing amount of user
nerated content yielding explicit and implicit network structures. We
nsider social activities and phenomena as proxies for user relatedness. Such
tivities are represented in so-called social interaction networks or evidence
tworks, with different degrees of explicitness. We focus on evidence networks
ntaining relations on users, which are represented by connections between
dividual nodes. Explicit interaction networks are then created by specific
er actions, for example, when building a friend network. On the other hand,
re implicit networks capture user traces or evidences of user actions as
served in Web portals, blogs, resource sharing systems, and many other social
rvices. These implicit networks can be applied for a broad range of analysis
thods instead of using expensive gold-standard information.
In this paper, we analyze different properties of a set of networks in social
dia. We show that there are dependencies and correlations between the
tworks. These allow for drawing reciprocal conclusions concerning pairs of
tworks, based on the assessment of structural correlations and ranking
terchangeability. Additionally, we show how these inter-network correlations
n be used for assessing the results of structural analysis techniques, e.g.,
mmunity mining methods.

@misc{mitzlaff2013userrelatedness, author = {Mitzlaff, Folke and Atzmueller, Martin and Benz, Dominik and Hotho, Andreas and Stumme, Gerd}, title = {User-Relatedness and Community Structure in Social Interaction Networks}, year = {2013}, note = {cite arxiv:1309.3888}, url = {http://arxiv.org/abs/1309.3888}, keywords = {2013, community, evidence, iteg, itegpub, l3s, myown, networks, social}, abstract = {With social media and the according social and ubiquitous applications
nding their way into everyday life, there is a rapidly growing amount of user
nerated content yielding explicit and implicit network structures. We
nsider social activities and phenomena as proxies for user relatedness. Such
tivities are represented in so-called social interaction networks or evidence
tworks, with different degrees of explicitness. We focus on evidence networks
ntaining relations on users, which are represented by connections between
dividual nodes. Explicit interaction networks are then created by specific
er actions, for example, when building a friend network. On the other hand,
re implicit networks capture user traces or evidences of user actions as
served in Web portals, blogs, resource sharing systems, and many other social
rvices. These implicit networks can be applied for a broad range of analysis
thods instead of using expensive gold-standard information.
In this paper, we analyze different properties of a set of networks in social
dia. We show that there are dependencies and correlations between the
tworks. These allow for drawing reciprocal conclusions concerning pairs of
tworks, based on the assessment of structural correlations and ranking
terchangeability. Additionally, we show how these inter-network correlations
n be used for assessing the results of structural analysis techniques, e.g.,
mmunity mining methods.} }

%0 = misc %A = Mitzlaff, Folke and Atzmueller, Martin and Benz, Dominik and Hotho, Andreas and Stumme, Gerd %D = 2013 %T = User-Relatedness and Community Structure in Social Interaction Networks %U = http://arxiv.org/abs/1309.3888

@inproceedings{scholz2013people, author = {Scholz, Christoph and Atzmueller, Martin and Kibanov, Mark and Stumme, Gerd}, title = {How Do People Link? Analysis of Contact Structures in Human Face-to-Face Proximity Networks}, booktitle = {Proc. ASONAM 2013}, publisher = {ACM Press}, address = {New York, NY, USA}, year = {2013}, keywords = {2013, analysis, face-to-face, iteg, itegpub, l3s, linkprediction, mining, myown, networks, sna} }

%0 = inproceedings %A = Scholz, Christoph and Atzmueller, Martin and Kibanov, Mark and Stumme, Gerd %B = Proc. ASONAM 2013 %C = New York, NY, USA %D = 2013 %I = ACM Press %T = How Do People Link? Analysis of Contact Structures in Human Face-to-Face Proximity Networks

Community mining is a prominent approach for identifying (user) communities in social and ubiquitous contexts. While there are a variety of methods for community mining and detection, the effective evaluation and validation of the mined communities is usually non-trivial. Often there is no evaluation data at hand in order to validate the discovered groups.

@inproceedings{mitzlaff2011community, author = {Mitzlaff, Folke and Atzmueller, Martin and Benz, Dominik and Hotho, Andreas and Stumme, Gerd}, title = {Community Assessment Using Evidence Networks}, editor = {Atzmueller, Martin and Hotho, Andreas and Strohmaier, Markus and Chin, Alvin}, booktitle = {Analysis of Social Media and Ubiquitous Data}, series = {Lecture Notes in Computer Science}, publisher = {Springer Berlin Heidelberg}, year = {2011}, volume = {6904}, pages = {79-98}, url = {http://dx.doi.org/10.1007/978-3-642-23599-3_5}, doi = {10.1007/978-3-642-23599-3_5}, isbn = {978-3-642-23598-6}, keywords = {2011, COMMUNE, evaluation, evidence, myown, networks}, abstract = {Community mining is a prominent approach for identifying (user) communities in social and ubiquitous contexts. While there are a variety of methods for community mining and detection, the effective evaluation and validation of the mined communities is usually non-trivial. Often there is no evaluation data at hand in order to validate the discovered groups.} }

%0 = inproceedings %A = Mitzlaff, Folke and Atzmueller, Martin and Benz, Dominik and Hotho, Andreas and Stumme, Gerd %B = Analysis of Social Media and Ubiquitous Data %D = 2011 %I = Springer Berlin Heidelberg %T = Community Assessment Using Evidence Networks %U = http://dx.doi.org/10.1007/978-3-642-23599-3_5

Community mining is a prominent approach for identifying (user) communities in social and ubiquitous contexts. While there are a variety of methods for community mining and detection, the effective evaluation and validation of the mined communities is usually non-trivial. Often there is no evaluation data at hand in order to validate the discovered groups. This paper proposes evidence networks using implicit information for the evaluation of communities. The presented evaluation approach is based on the idea of reconstructing existing social structures for the assessment and evaluation of a given clustering. We analyze and compare the presented evidence networks using user data from the real-world social
okmarking application BibSonomy. The results indicate that the evidence
tworks reflect the relative rating of the explicit ones very well.

@inproceedings{mitzlaff2010community, author = {Mitzlaff, Folke and Atzmüller, Martin and Benz, Dominik and Hotho, Andreas and Stumme, Gerd}, title = {Community Assessment using Evidence Networks}, booktitle = {Proceedings of the Workshop on Mining Ubiquitous and Social Environments (MUSE2010)}, address = {Barcelona, Spain}, year = {2010}, url = {http://www.kde.cs.uni-kassel.de/ws/muse2010}, keywords = {2010, assessment, bibsonomy, community, evaluation, evidence, itegpub, l3s, myown, networks}, abstract = {Community mining is a prominent approach for identifying (user) communities in social and ubiquitous contexts. While there are a variety of methods for community mining and detection, the effective evaluation and validation of the mined communities is usually non-trivial. Often there is no evaluation data at hand in order to validate the discovered groups. This paper proposes evidence networks using implicit information for the evaluation of communities. The presented evaluation approach is based on the idea of reconstructing existing social structures for the assessment and evaluation of a given clustering. We analyze and compare the presented evidence networks using user data from the real-world social
okmarking application BibSonomy. The results indicate that the evidence
tworks reflect the relative rating of the explicit ones very well.} }

%0 = inproceedings %A = Mitzlaff, Folke and Atzmüller, Martin and Benz, Dominik and Hotho, Andreas and Stumme, Gerd %B = Proceedings of the Workshop on Mining Ubiquitous and Social Environments (MUSE2010) %C = Barcelona, Spain %D = 2010 %T = Community Assessment using Evidence Networks %U = http://www.kde.cs.uni-kassel.de/ws/muse2010

@inproceedings{eisterlehner2010visit, author = {Mitzlaff, Folke and Benz, Dominik and Stumme, Gerd and Hotho, Andreas}, title = {Visit me, click me, be my friend: An analysis of evidence networks of user relationships in Bibsonomy}, booktitle = {Proceedings of the 21st ACM conference on Hypertext and hypermedia}, address = {Toronto, Canada}, year = {2010}, keywords = {2010, analysis, bibsonomy, evidence, itegpub, l3s, links, myown, networks, semantic, sna, web} }

%0 = inproceedings %A = Mitzlaff, Folke and Benz, Dominik and Stumme, Gerd and Hotho, Andreas %B = Proceedings of the 21st ACM conference on Hypertext and hypermedia %C = Toronto, Canada %D = 2010 %T = Visit me, click me, be my friend: An analysis of evidence networks of user relationships in Bibsonomy

Network science is an interdisciplinary endeavor, with methods and applications drawn from across the natural, social, and information sciences. A prominent problem in network science is the algorithmic detection of tightly connected groups of nodes known as communities. We developed a generalized framework of network quality functions that allowed us to study the community structure of arbitrary multislice networks, which are combinations of individual networks coupled through links that connect each node in one network slice to itself in other slices. This framework allows studies of community structure in a general setting encompassing networks that evolve over time, have multiple types of links (multiplexity), and have multiple scales.

@article{Mucha14052010, author = {Mucha, Peter J. and Richardson, Thomas and Macon, Kevin and Porter, Mason A. and Onnela, Jukka-Pekka}, title = {Community Structure in Time-Dependent, Multiscale, and Multiplex Networks}, journal = {Science}, year = {2010}, volume = {328}, number = {5980}, pages = {876-878}, url = {http://www.sciencemag.org/content/328/5980/876.abstract}, doi = {10.1126/science.1184819}, keywords = {communities, community, evolving, graphs, networks, time}, abstract = {Network science is an interdisciplinary endeavor, with methods and applications drawn from across the natural, social, and information sciences. A prominent problem in network science is the algorithmic detection of tightly connected groups of nodes known as communities. We developed a generalized framework of network quality functions that allowed us to study the community structure of arbitrary multislice networks, which are combinations of individual networks coupled through links that connect each node in one network slice to itself in other slices. This framework allows studies of community structure in a general setting encompassing networks that evolve over time, have multiple types of links (multiplexity), and have multiple scales.} }

%0 = article %A = Mucha, Peter J. and Richardson, Thomas and Macon, Kevin and Porter, Mason A. and Onnela, Jukka-Pekka %D = 2010 %T = Community Structure in Time-Dependent, Multiscale, and Multiplex Networks %U = http://www.sciencemag.org/content/328/5980/876.abstract

Sensors have begun to infiltrate people's everyday lives. They can provide information about a car's condition, can enable smart buildings, and are being used in various mobile applications, to name a few. Generally, sensors provide information about various aspects of the real world. Online social networks, another emerging trend over the past six or seven years, can provide insights into the communication links and patterns between people. They have enabled novel developments in communications as well as transforming the Web from a technical infrastructure to a social platform, very much along the lines of the original Web as proposed by Tim Berners-Lee, which is now often referred to as the Social Web. In this position paper, we highlight some of the interesting research areas where sensors and social networks can fruitfully interface, from sensors providing contextual information in context-aware and personalized social applications, to using social networks as "storage infrastructures" for sensor information.

@inproceedings{breslin2009integrating, author = {Breslin, John G. and Decker, Stefan and Hauswirth, Manfred and Hynes, Gearoid and Phuoc, Danh Le and Passant, Alexandre and Polleres, Axel and Rabsch, Cornelius and Reynolds, Vinny}, title = {Integrating Social Networks and Sensor Networks}, booktitle = {Proceedings on the W3C Workshop on the Future of Social Networking}, year = {2009}, url = {http://www.w3.org/2008/09/msnws/papers/sensors.html}, keywords = {network, networks, sensor, sensors, social, venus}, abstract = {Sensors have begun to infiltrate people's everyday lives. They can provide information about a car's condition, can enable smart buildings, and are being used in various mobile applications, to name a few. Generally, sensors provide information about various aspects of the real world. Online social networks, another emerging trend over the past six or seven years, can provide insights into the communication links and patterns between people. They have enabled novel developments in communications as well as transforming the Web from a technical infrastructure to a social platform, very much along the lines of the original Web as proposed by Tim Berners-Lee, which is now often referred to as the Social Web. In this position paper, we highlight some of the interesting research areas where sensors and social networks can fruitfully interface, from sensors providing contextual information in context-aware and personalized social applications, to using social networks as "storage infrastructures" for sensor information.} }

%0 = inproceedings %A = Breslin, John G. and Decker, Stefan and Hauswirth, Manfred and Hynes, Gearoid and Phuoc, Danh Le and Passant, Alexandre and Polleres, Axel and Rabsch, Cornelius and Reynolds, Vinny %B = Proceedings on the W3C Workshop on the Future of Social Networking %D = 2009 %T = Integrating Social Networks and Sensor Networks %U = http://www.w3.org/2008/09/msnws/papers/sensors.html

Heterogeneous networks play a key role in the evolution of communities and
e decisions individuals make. These networks link different types of
tities, for example, people and the events they attend. Network analysis
gorithms usually project such networks unto simple graphs composed of
tities of a single type. In the process, they conflate relations between
tities of different types and loose important structural information. We
velop a mathematical framework that can be used to compactly represent and
alyze heterogeneous networks that combine multiple entity and link types. We
neralize Bonacich centrality, which measures connectivity between nodes by
e number of paths between them, to heterogeneous networks and use this
asure to study network structure. Specifically, we extend the popular
dularity-maximization method for community detection to use this centrality
tric. We also rank nodes based on their connectivity to other nodes. One
vantage of this centrality metric is that it has a tunable parameter we can
e to set the length scale of interactions. By studying how rankings change
th this parameter allows us to identify important nodes in the network. We
ply the proposed method to analyze the structure of several heterogeneous
tworks. We show that exploiting additional sources of evidence corresponding
links between, as well as among, different entity types yields new insights
to network structure.

@misc{Ghosh2009, author = {Ghosh, Rumi and Lerman, Kristina}, title = {Structure of Heterogeneous Networks}, year = {2009}, note = {cite arxiv:0906.2212
}, url = {http://arxiv.org/abs/0906.2212}, keywords = {graph, graphs, heterogenous, measures, multi-mode, networks, sna}, abstract = { Heterogeneous networks play a key role in the evolution of communities and
e decisions individuals make. These networks link different types of
tities, for example, people and the events they attend. Network analysis
gorithms usually project such networks unto simple graphs composed of
tities of a single type. In the process, they conflate relations between
tities of different types and loose important structural information. We
velop a mathematical framework that can be used to compactly represent and
alyze heterogeneous networks that combine multiple entity and link types. We
neralize Bonacich centrality, which measures connectivity between nodes by
e number of paths between them, to heterogeneous networks and use this
asure to study network structure. Specifically, we extend the popular
dularity-maximization method for community detection to use this centrality
tric. We also rank nodes based on their connectivity to other nodes. One
vantage of this centrality metric is that it has a tunable parameter we can
e to set the length scale of interactions. By studying how rankings change
th this parameter allows us to identify important nodes in the network. We
ply the proposed method to analyze the structure of several heterogeneous
tworks. We show that exploiting additional sources of evidence corresponding
links between, as well as among, different entity types yields new insights
to network structure.
} }

%0 = misc %A = Ghosh, Rumi and Lerman, Kristina %D = 2009 %T = Structure of Heterogeneous Networks %U = http://arxiv.org/abs/0906.2212

Operators of online social networks are increasingly sharing potentially
nsitive information about users and their relationships with advertisers,
plication developers, and data-mining researchers. Privacy is typically
otected by anonymization, i.e., removing names, addresses, etc.
We present a framework for analyzing privacy and anonymity in social networks
d develop a new re-identification algorithm targeting anonymized
cial-network graphs. To demonstrate its effectiveness on real-world networks,
show that a third of the users who can be verified to have accounts on both
itter, a popular microblogging service, and Flickr, an online photo-sharing
te, can be re-identified in the anonymous Twitter graph with only a 12% error
te.
Our de-anonymization algorithm is based purely on the network topology, does
t require creation of a large number of dummy "sybil" nodes, is robust to
ise and all existing defenses, and works even when the overlap between the
rget network and the adversary's auxiliary information is small.

@misc{Narayanan2009, author = {Narayanan, Arvind and Shmatikov, Vitaly}, title = {De-anonymizing Social Networks}, year = {2009}, note = {cite arxiv:0903.3276
mment: Published in the 30th IEEE Symposium on Security and Privacy, 2009.
The definitive version is available at:
http://www.cs.utexas.edu/~shmat/shmat_oak09.pdf Frequently Asked Questions
are answered at: http://www.cs.utexas.edu/~shmat/socialnetworks-faq.html}, url = {http://arxiv.org/abs/0903.3276}, keywords = {anonymizing, anonymous, de-anonymizing, networks, sna, social}, abstract = { Operators of online social networks are increasingly sharing potentially
nsitive information about users and their relationships with advertisers,
plication developers, and data-mining researchers. Privacy is typically
otected by anonymization, i.e., removing names, addresses, etc.
We present a framework for analyzing privacy and anonymity in social networks
d develop a new re-identification algorithm targeting anonymized
cial-network graphs. To demonstrate its effectiveness on real-world networks,
show that a third of the users who can be verified to have accounts on both
itter, a popular microblogging service, and Flickr, an online photo-sharing
te, can be re-identified in the anonymous Twitter graph with only a 12% error
te.
Our de-anonymization algorithm is based purely on the network topology, does
t require creation of a large number of dummy "sybil" nodes, is robust to
ise and all existing defenses, and works even when the overlap between the
rget network and the adversary's auxiliary information is small.
} }

%0 = misc %A = Narayanan, Arvind and Shmatikov, Vitaly %D = 2009 %T = De-anonymizing Social Networks %U = http://arxiv.org/abs/0903.3276

As online social networking emerges, there has been increased interest to utilize the underlying social structure as well as the available social information to improve search. In this paper, we focus on improving the performance of information collection from the neighborhood of a user in a dynamic social network. To this end, we introduce sampling based algorithms to quickly approximate quantities of interest from the vicinity of a user's social graph. We then introduce and analyze variants of this basic scheme exploring correlations across our samples. Models of centralized and distributed social networks are considered. We show that our algorithms can be utilized to rank items in the neighborhood of a user, assuming that information for each user in the network is available. Using real and synthetic data sets, we validate the results of our analysis and demonstrate the efficiency of our algorithms in approximating quantities of interest. The methods we describe are general and can probably be easily adopted in a variety of strategies aiming to efficiently collect information from a social graph.

@inproceedings{das2008efficient, author = {Das, Gautam and Koudas, Nick and Papagelis, Manos and Puttaswamy, Sushruth}, title = {Efficient sampling of information in social networks}, booktitle = {SSM '08: Proceeding of the 2008 ACM workshop on Search in social media}, publisher = {ACM}, address = {New York, NY, USA}, year = {2008}, pages = {67--74}, url = {http://portal.acm.org/citation.cfm?id=1458583.1458594}, doi = {http://doi.acm.org/10.1145/1458583.1458594}, isbn = {978-1-60558-258-0}, keywords = {analysis, network, networks, sampling, sna, social}, abstract = {As online social networking emerges, there has been increased interest to utilize the underlying social structure as well as the available social information to improve search. In this paper, we focus on improving the performance of information collection from the neighborhood of a user in a dynamic social network. To this end, we introduce sampling based algorithms to quickly approximate quantities of interest from the vicinity of a user's social graph. We then introduce and analyze variants of this basic scheme exploring correlations across our samples. Models of centralized and distributed social networks are considered. We show that our algorithms can be utilized to rank items in the neighborhood of a user, assuming that information for each user in the network is available. Using real and synthetic data sets, we validate the results of our analysis and demonstrate the efficiency of our algorithms in approximating quantities of interest. The methods we describe are general and can probably be easily adopted in a variety of strategies aiming to efficiently collect information from a social graph.} }

%0 = inproceedings %A = Das, Gautam and Koudas, Nick and Papagelis, Manos and Puttaswamy, Sushruth %B = SSM '08: Proceeding of the 2008 ACM workshop on Search in social media %C = New York, NY, USA %D = 2008 %I = ACM %T = Efficient sampling of information in social networks %U = http://portal.acm.org/citation.cfm?id=1458583.1458594

In a social network, nodes correspond topeople or other social entities, and edges correspond to social links between them. In an effort to preserve privacy, the practice of anonymization replaces names with meaningless unique identifiers. We describe a family of attacks such that even from a single anonymized copy of a social network, it is possible for an adversary to learn whether edges exist or not between specific targeted pairs of nodes.

@inproceedings{Backstrom:2007:WAT:1242572.1242598, author = {Backstrom, Lars and Dwork, Cynthia and Kleinberg, Jon}, title = {Wherefore art thou r3579x?: anonymized social networks, hidden patterns, and structural steganography}, booktitle = {Proceedings of the 16th international conference on World Wide Web}, series = {WWW '07}, publisher = {ACM}, address = {New York, NY, USA}, year = {2007}, pages = {181--190}, url = {http://doi.acm.org/10.1145/1242572.1242598}, doi = {10.1145/1242572.1242598}, isbn = {978-1-59593-654-7}, keywords = {anonymizing, anonymous, de-anonymizing, networks, sna, social}, abstract = {In a social network, nodes correspond topeople or other social entities, and edges correspond to social links between them. In an effort to preserve privacy, the practice of anonymization replaces names with meaningless unique identifiers. We describe a family of attacks such that even from a single anonymized copy of a social network, it is possible for an adversary to learn whether edges exist or not between specific targeted pairs of nodes.} }

%0 = inproceedings %A = Backstrom, Lars and Dwork, Cynthia and Kleinberg, Jon %B = Proceedings of the 16th international conference on World Wide Web %C = New York, NY, USA %D = 2007 %I = ACM %T = Wherefore art thou r3579x?: anonymized social networks, hidden patterns, and structural steganography %U = http://doi.acm.org/10.1145/1242572.1242598

@unpublished{FalBar07, author = {Falkowski, Tanja and Barth, Anja}, title = {Density-based Temporal Graph Clustering for Subgroup Detection in Social Networks}, year = {2007}, note = {Presented at The 4th conference on Applications of Social Network Analysis (ASNA)}, keywords = {clustering, networks, sna, social, subgroups} }

%0 = unpublished %A = Falkowski, Tanja and Barth, Anja %D = 2007 %T = Density-based Temporal Graph Clustering for Subgroup Detection in Social Networks

@article{palla2007quantifying, author = {Palla, Gergely and Barabási, Albert-lászló and Vicsek, Tamás and Hungary, Budapest}, title = {Quantifying social group evolution}, year = {2007}, volume = {446}, pages = {2007}, url = {http://130.203.133.150/viewdoc/summary?doi=10.1.1.119.7541}, keywords = {dynamic, evolution, group, networks, social} }

%0 = article %A = Palla, Gergely and Barabási, Albert-lászló and Vicsek, Tamás and Hungary, Budapest %D = 2007 %T = Quantifying social group evolution %U = http://130.203.133.150/viewdoc/summary?doi=10.1.1.119.7541

@article{dallasta-2006-73, author = {Dall'Asta, Luca and Baronchelli, Andrea and Barrat, Alain and Loreto, Vittorio}, title = {Agreement dynamics on small-world networks}, journal = {Europhysics Letters}, year = {2006}, volume = {73}, pages = {969}, url = {doi:10.1209/epl/i2005-10481-7}, keywords = {network, networks, small, sna, world} }

%0 = article %A = Dall'Asta, Luca and Baronchelli, Andrea and Barrat, Alain and Loreto, Vittorio %D = 2006 %T = Agreement dynamics on small-world networks %U = doi:10.1209/epl/i2005-10481-7

We describe online collaborative communities by tripartite networks, the nodes being persons, items and tags. We introduce projection methods in order to uncover the structures of the networks, i.e. communities of users, genre families... The structuring of the network is visualised by using a tree representation. The notion of diversity in the system is also discussed.

@inproceedings{paper:lambiotte:2006, author = {Lambiotte, Renaud and Ausloos, Marcel}, title = {Collaborative Tagging as a Tripartite Network}, booktitle = {Computational Science – ICCS 2006}, publisher = {Springer Berlin / Heidelberg}, year = {2006}, pages = {1114-1117}, keywords = {analysis, collaborative, folksonomy, networks, sna, social, tagging, tripartite}, abstract = {We describe online collaborative communities by tripartite networks, the nodes being persons, items and tags. We introduce projection methods in order to uncover the structures of the networks, i.e. communities of users, genre families... The structuring of the network is visualised by using a tree representation. The notion of diversity in the system is also discussed.} }

%0 = inproceedings %A = Lambiotte, Renaud and Ausloos, Marcel %B = Computational Science – ICCS 2006 %D = 2006 %I = Springer Berlin / Heidelberg %T = Collaborative Tagging as a Tripartite Network

@article{newman03structure, author = {Newman, M. E. J.}, title = {The structure and function of complex networks}, journal = {SIAM Review}, year = {2003}, volume = {45}, pages = {167}, url = {http://www.citebase.org/abstract?id=oai:arXiv.org:cond-mat/0303516}, keywords = {complex, free, law, long, networks, power, scale, tail} }

%0 = article %A = Newman, M. E. J. %D = 2003 %T = The structure and function of complex networks %U = http://www.citebase.org/abstract?id=oai:arXiv.org:cond-mat/0303516

@article{albert:02statistical, author = {Albert, Reka and Barabasi, Albert-Laszlo}, title = {Statistical mechanics of complex networks}, journal = {Reviews of Modern Physics}, publisher = {APS}, year = {2002}, volume = {74}, number = {1}, pages = {47}, url = {http://link.aps.org/abstract/RMP/v74/p47}, keywords = {albert, barabasi, complex, mechanics, networks, statistical} }

%0 = article %A = Albert, Reka and Barabasi, Albert-Laszlo %D = 2002 %I = APS %T = Statistical mechanics of complex networks %U = http://link.aps.org/abstract/RMP/v74/p47

We describe a novel approach to visualize bibliographic networks that facilitates the simultaneous identification of clusters (e.g., topic areas) and prominent entities (e.g., surveys or landmark papers). While employing the landscape metaphor proposed in several earlier works, we introduce new means to determine relevant parameters of the landscape. Moreover, we are able to compute prominent entities, clustering of entities, and the landscape's surface in a surprisingly simple and uniform way. The effectiveness of our network visualizations is illustrated on data from the graph drawing literature.

@inproceedings{Brandes:2002:VBN:509740.509765, author = {Brandes, U. and Willhalm, T.}, title = {Visualization of bibliographic networks with a reshaped landscape metaphor}, booktitle = {Proceedings of the symposium on Data Visualisation 2002}, series = {VISSYM '02}, publisher = {Eurographics Association}, address = {Aire-la-Ville, Switzerland, Switzerland}, year = {2002}, pages = {159--ff}, url = {http://portal.acm.org/citation.cfm?id=509740.509765}, isbn = {1-58113-536-X}, keywords = {bibliographic, bibliography, citation, graph, networks, sna}, abstract = {We describe a novel approach to visualize bibliographic networks that facilitates the simultaneous identification of clusters (e.g., topic areas) and prominent entities (e.g., surveys or landmark papers). While employing the landscape metaphor proposed in several earlier works, we introduce new means to determine relevant parameters of the landscape. Moreover, we are able to compute prominent entities, clustering of entities, and the landscape's surface in a surprisingly simple and uniform way. The effectiveness of our network visualizations is illustrated on data from the graph drawing literature.} }

%0 = inproceedings %A = Brandes, U. and Willhalm, T. %B = Proceedings of the symposium on Data Visualisation 2002 %C = Aire-la-Ville, Switzerland, Switzerland %D = 2002 %I = Eurographics Association %T = Visualization of bibliographic networks with a reshaped landscape metaphor %U = http://portal.acm.org/citation.cfm?id=509740.509765

@article{newman02assortative, author = {Newman, M. E. J.}, title = {Assortative Mixing in Networks}, journal = {Phys. Rev. Lett.}, year = {2002}, volume = {89}, pages = {208701}, keywords = {assortative, mixing, networks, sna} }

%0 = article %A = Newman, M. E. J. %D = 2002 %T = Assortative Mixing in Networks