%0 
%0 Conference Proceedings
%A Christoph Scholz; Martin Atzmueller; Alain Barrat; Ciro Cattuto & Gerd Stumme,
%D 2013
%T New Insights and Methods For Predicting Face-To-Face Contacts
%E 
%B Proc. 7th Intl. AAAI Conference on Weblogs and Social Media
%C Palo Alto, CA, USA
%I AAAI Press
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%F christophscholzandmartinatzmuellerandalainbarratandcirocattutoandgerdstumme2013insights
%K 2013, conferator, contact, face-to-face, iteg, itegpub, l3s, link, myown, networks, prediction, venus
%X 
%Z 
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%0 
%0 Generic
%A Mitzlaff, Folke; Atzmueller, Martin; Benz, Dominik; Hotho, Andreas & Stumme, Gerd
%D 2013
%T User-Relatedness and Community Structure in Social Interaction Networks
%E
%B 
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%F mitzlaff2013userrelatedness
%K 2013, community, evidence, iteg, itegpub, l3s, myown, networks, social
%X With social media and the according social and ubiquitous applications
finding their way into everyday life, there is a rapidly growing amount of user
generated content yielding explicit and implicit network structures. We
consider social activities and phenomena as proxies for user relatedness. Such
activities are represented in so-called social interaction networks or evidence
networks, with different degrees of explicitness. We focus on evidence networks
containing relations on users, which are represented by connections between
individual nodes. Explicit interaction networks are then created by specific
user actions, for example, when building a friend network. On the other hand,
more implicit networks capture user traces or evidences of user actions as
observed in Web portals, blogs, resource sharing systems, and many other social
services. These implicit networks can be applied for a broad range of analysis
methods instead of using expensive gold-standard information.
  In this paper, we analyze different properties of a set of networks in social
media. We show that there are dependencies and correlations between the
networks. These allow for drawing reciprocal conclusions concerning pairs of
networks, based on the assessment of structural correlations and ranking
interchangeability. Additionally, we show how these inter-network correlations
can be used for assessing the results of structural analysis techniques, e.g.,
community mining methods.
%Z cite arxiv:1309.3888
%U http://arxiv.org/abs/1309.3888
%+
%^

%0 
%0 Conference Proceedings
%A Scholz, Christoph; Atzmueller, Martin; Kibanov, Mark & Stumme, Gerd
%D 2013
%T How Do People Link? Analysis of Contact Structures in Human Face-to-Face Proximity Networks
%E 
%B Proc. ASONAM 2013
%C New York, NY, USA
%I ACM Press
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%F scholz2013people
%K 2013, analysis, face-to-face, iteg, itegpub, l3s, linkprediction, mining, myown, networks, sna
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%^

%0 
%0 Conference Proceedings
%A Mitzlaff, Folke; Atzmueller, Martin; Benz, Dominik; Hotho, Andreas & Stumme, Gerd
%D 2011
%T Community Assessment Using Evidence Networks
%E Atzmueller, Martin; Hotho, Andreas; Strohmaier, Markus & Chin, Alvin
%B Analysis of Social Media and Ubiquitous Data
%C 
%I Springer Berlin Heidelberg
%V 6904
%6
%N 
%P 79-98
%& 
%Y 
%S Lecture Notes in Computer Science
%7
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%@ 978-3-642-23598-6
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%1 
%2 Community Assessment Using Evidence Networks - Springer
%3 inproceedings
%4 
%# 
%$ 
%F mitzlaff2011community
%K 2011, COMMUNE, evaluation, evidence, myown, networks
%X 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.
%Z 
%U http://dx.doi.org/10.1007/978-3-642-23599-3_5
%+
%^

%0 
%0 Conference Proceedings
%A Mitzlaff, Folke; Atzmüller, Martin; Benz, Dominik; Hotho, Andreas & Stumme, Gerd
%D 2010
%T Community Assessment using Evidence Networks
%E 
%B Proceedings of the Workshop on Mining Ubiquitous and Social Environments (MUSE2010)
%C Barcelona, Spain
%I 
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%F mitzlaff2010community
%K 2010, assessment, bibsonomy, community, evaluation, evidence, itegpub, l3s, myown, networks
%X 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
bookmarking application BibSonomy. The results indicate that the evidence
networks reflect the relative rating of the explicit ones very well.
%Z 
%U http://www.kde.cs.uni-kassel.de/ws/muse2010
%+
%^

%0 
%0 Conference Proceedings
%A Mitzlaff, Folke; Benz, Dominik; Stumme, Gerd & Hotho, Andreas
%D 2010
%T Visit me, click me, be my friend: An analysis of evidence networks of user relationships in Bibsonomy
%E 
%B Proceedings of the 21st ACM conference on Hypertext and hypermedia
%C Toronto, Canada
%I 
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%4 
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%F eisterlehner2010visit
%K 2010, analysis, bibsonomy, evidence, itegpub, l3s, links, myown, networks, semantic, sna, web
%X 
%Z 
%U 
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%^

%0 
%0 Journal Article
%A Mucha, Peter J.; Richardson, Thomas; Macon, Kevin; Porter, Mason A. & Onnela, Jukka-Pekka
%D 2010
%T Community Structure in Time-Dependent, Multiscale, and Multiplex Networks
%E 
%B Science
%C 
%I 
%V 328
%6
%N 5980
%P 876-878
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%2 Community Structure in Time-Dependent, Multiscale, and Multiplex Networks
%3 article
%4 
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%$ 
%F Mucha14052010
%K communities, community, evolving, graphs, networks, time
%X 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.
%Z 
%U http://www.sciencemag.org/content/328/5980/876.abstract
%+
%^

%0 
%0 Conference Proceedings
%A Breslin, John G.; Decker, Stefan; Hauswirth, Manfred; Hynes, Gearoid; Phuoc, Danh Le; Passant, Alexandre; Polleres, Axel; Rabsch, Cornelius & Reynolds, Vinny
%D 2009
%T Integrating Social Networks and Sensor Networks
%E 
%B Proceedings on the W3C Workshop on the Future of Social Networking
%C 
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%4 
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%F breslin2009integrating
%K network, networks, sensor, sensors, social, venus
%X 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.
%Z 
%U http://www.w3.org/2008/09/msnws/papers/sensors.html
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%0 
%0 Generic
%A Ghosh, Rumi & Lerman, Kristina
%D 2009
%T Structure of Heterogeneous Networks
%E
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%2 [0906.2212] Structure of Heterogeneous Networks
%3 misc
%4 
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%$ 
%F Ghosh2009
%K graph, graphs, heterogenous, measures, multi-mode, networks, sna
%X   Heterogeneous networks play a key role in the evolution of communities and
the decisions individuals make. These networks link different types of
entities, for example, people and the events they attend. Network analysis
algorithms usually project such networks unto simple graphs composed of
entities of a single type. In the process, they conflate relations between
entities of different types and loose important structural information. We
develop a mathematical framework that can be used to compactly represent and
analyze heterogeneous networks that combine multiple entity and link types. We
generalize Bonacich centrality, which measures connectivity between nodes by
the number of paths between them, to heterogeneous networks and use this
measure to study network structure. Specifically, we extend the popular
modularity-maximization method for community detection to use this centrality
metric. We also rank nodes based on their connectivity to other nodes. One
advantage of this centrality metric is that it has a tunable parameter we can
use to set the length scale of interactions. By studying how rankings change
with this parameter allows us to identify important nodes in the network. We
apply the proposed method to analyze the structure of several heterogeneous
networks. We show that exploiting additional sources of evidence corresponding
to links between, as well as among, different entity types yields new insights
into network structure.

%Z cite arxiv:0906.2212

%U http://arxiv.org/abs/0906.2212
%+
%^

%0 
%0 Generic
%A Narayanan, Arvind & Shmatikov, Vitaly
%D 2009
%T De-anonymizing Social Networks
%E
%B 
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%2 De-anonymizing Social Networks
%3 misc
%4 
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%$ 
%F Narayanan2009
%K anonymizing, anonymous, de-anonymizing, networks, sna, social
%X   Operators of online social networks are increasingly sharing potentially
sensitive information about users and their relationships with advertisers,
application developers, and data-mining researchers. Privacy is typically
protected by anonymization, i.e., removing names, addresses, etc.
  We present a framework for analyzing privacy and anonymity in social networks
and develop a new re-identification algorithm targeting anonymized
social-network graphs. To demonstrate its effectiveness on real-world networks,
we show that a third of the users who can be verified to have accounts on both
Twitter, a popular microblogging service, and Flickr, an online photo-sharing
site, can be re-identified in the anonymous Twitter graph with only a 12% error
rate.
  Our de-anonymization algorithm is based purely on the network topology, does
not require creation of a large number of dummy "sybil" nodes, is robust to
noise and all existing defenses, and works even when the overlap between the
target network and the adversary's auxiliary information is small.

%Z cite arxiv:0903.3276
Comment: 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
%U http://arxiv.org/abs/0903.3276
%+
%^

%0 
%0 Conference Proceedings
%A Das, Gautam; Koudas, Nick; Papagelis, Manos & Puttaswamy, Sushruth
%D 2008
%T Efficient sampling of information in social networks
%E 
%B SSM '08: Proceeding of the 2008 ACM workshop on Search in social media
%C New York, NY, USA
%I ACM
%V 
%6
%N 
%P 67--74
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%2 Efficient sampling of information in social networks
%3 inproceedings
%4 
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%$ 
%F das2008efficient
%K analysis, network, networks, sampling, sna, social
%X 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.
%Z 
%U http://portal.acm.org/citation.cfm?id=1458583.1458594
%+
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%0 
%0 Conference Proceedings
%A Backstrom, Lars; Dwork, Cynthia & Kleinberg, Jon
%D 2007
%T Wherefore art thou r3579x?: anonymized social networks, hidden patterns, and structural steganography
%E 
%B Proceedings of the 16th international conference on World Wide Web
%C New York, NY, USA
%I ACM
%V 
%6
%N 
%P 181--190
%& 
%Y 
%S WWW '07
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%2 Wherefore art thou r3579x?
%3 inproceedings
%4 
%# 
%$ 
%F Backstrom:2007:WAT:1242572.1242598
%K anonymizing, anonymous, de-anonymizing, networks, sna, social
%X 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.
%Z 
%U http://doi.acm.org/10.1145/1242572.1242598
%+
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%0 
%0 Manuscript
%A Falkowski, Tanja & Barth, Anja
%D 2007
%T Density-based Temporal Graph Clustering for Subgroup Detection in Social Networks
%E
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%2 Tanja Falkowski
%3 unpublished
%4 
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%F FalBar07
%K clustering, networks, sna, social, subgroups
%X 
%Z Presented at The 4th conference on Applications of Social Network Analysis (ASNA)
%U 
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%0 
%0 Journal Article
%A Palla, Gergely; Barabási, Albert-lászló; Vicsek, Tamás & Hungary, Budapest
%D 2007
%T Quantifying social group evolution
%E 
%B 
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%I 
%V 446
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%P 2007
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%2 CiteSeerX — Quantifying social group evolution
%3 article
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%F palla2007quantifying
%K dynamic, evolution, group, networks, social
%X 
%Z 
%U http://130.203.133.150/viewdoc/summary?doi=10.1.1.119.7541
%+
%^

%0 
%0 Journal Article
%A Dall'Asta, Luca; Baronchelli, Andrea; Barrat, Alain & Loreto, Vittorio
%D 2006
%T Agreement dynamics on small-world networks
%E 
%B Europhysics Letters
%C 
%I 
%V 73
%6
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%P 969
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%F dallasta-2006-73
%K network, networks, small, sna, world
%X 
%Z 
%U doi:10.1209/epl/i2005-10481-7
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%0 
%0 Conference Proceedings
%A Lambiotte, Renaud & Ausloos, Marcel
%D 2006
%T Collaborative Tagging as a Tripartite Network
%E 
%B Computational Science – ICCS 2006
%C 
%I Springer Berlin / Heidelberg
%V 
%6
%N 
%P 1114-1117
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%F paper:lambiotte:2006
%K analysis, collaborative, folksonomy, networks, sna, social, tagging, tripartite
%X 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.
%Z 
%U 
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%0 
%0 Journal Article
%A Newman, M. E. J.
%D 2003
%T The structure and function of complex networks
%E 
%B SIAM Review
%C 
%I 
%V 45
%6
%N 
%P 167
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%F newman03structure
%K complex, free, law, long, networks, power, scale, tail
%X 
%Z 
%U http://www.citebase.org/abstract?id=oai:arXiv.org:cond-mat/0303516
%+
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%0 
%0 Journal Article
%A Albert, Reka & Barabasi, Albert-Laszlo
%D 2002
%T Statistical mechanics of complex networks
%E 
%B Reviews of Modern Physics
%C 
%I APS
%V 74
%6
%N 1
%P 47
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%F albert:02statistical
%K albert, barabasi, complex, mechanics, networks, statistical
%X 
%Z 
%U http://link.aps.org/abstract/RMP/v74/p47
%+
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%0 
%0 Conference Proceedings
%A Brandes, U. & Willhalm, T.
%D 2002
%T Visualization of bibliographic networks with a reshaped landscape metaphor
%E 
%B Proceedings of the symposium on Data Visualisation 2002
%C Aire-la-Ville, Switzerland, Switzerland
%I Eurographics Association
%V 
%6
%N 
%P 159--ff
%& 
%Y 
%S VISSYM '02
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%2 Visualization of bibliographic networks with a reshaped landscape metaphor
%3 inproceedings
%4 
%# 
%$ 
%F Brandes:2002:VBN:509740.509765
%K bibliographic, bibliography, citation, graph, networks, sna
%X 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.
%Z 
%U http://portal.acm.org/citation.cfm?id=509740.509765
%+
%^

%0 
%0 Journal Article
%A Newman, M. E. J.
%D 2002
%T Assortative Mixing in Networks
%E 
%B Phys. Rev. Lett.
%C 
%I 
%V 89
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%N 
%P 208701
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%F newman02assortative
%K assortative, mixing, networks, sna
%X 
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