Many Web users have accounts with multiple different social networking services. This scenario has prompted development of "social aggregation" services such as FriendFeed that aggregate the information available through various services. Using five weeks of activity of more than 100,000 FriendFeed users, we consider questions such as what types of services users aggregate content from, the relative popularity of services, who follows the aggregated content feeds, and why.

@inproceedings{citeulike:5980713, author = {Gupta, T. and Garg, S. and Mahanti, A. and Carlsson, N. and Arlitt, M.}, title = {Characterization of FriendFeed - A Web-based Social Aggregation Service}, journal = {International AAAI Conference on Weblogs and Social Media}, year = {2009}, url = {http://www.aaai.org/ocs/index.php/ICWSM/09/paper/view/193}, keywords = {analysis, friendfeed, network, sna}, abstract = {Many Web users have accounts with multiple different social networking services. This scenario has prompted development of "social aggregation" services such as FriendFeed that aggregate the information available through various services. Using five weeks of activity of more than 100,000 FriendFeed users, we consider questions such as what types of services users aggregate content from, the relative popularity of services, who follows the aggregated content feeds, and why.} }

%0 = inproceedings %A = Gupta, T. and Garg, S. and Mahanti, A. and Carlsson, N. and Arlitt, M. %D = 2009 %T = Characterization of FriendFeed - A Web-based Social Aggregation Service %U = http://www.aaai.org/ocs/index.php/ICWSM/09/paper/view/193

@article{blondel2008fuc, author = {Blondel, V.D. and Guillaume, J.L. and Lambiotte, R. and Mech, E.L.J.S.}, title = {Fast unfolding of communities in large networks}, journal = {J. Stat. Mech}, year = {2008}, pages = {P10008}, keywords = {community, detection, modularity, network} }

%0 = article %A = Blondel, V.D. and Guillaume, J.L. and Lambiotte, R. and Mech, E.L.J.S. %D = 2008 %T = Fast unfolding of communities in large networks

Social bookmarking systems constitute an established
rt of the Web 2.0. In such systems
ers describe bookmarks by keywords
lled tags. The structure behind these social
stems, called folksonomies, can be viewed
a tripartite hypergraph of user, tag and resource
des. This underlying network shows
ecific structural properties that explain its
owth and the possibility of serendipitous
ploration.
day’s search engines represent the gateway
retrieve information from the World Wide
b. Short queries typically consisting of
o to three words describe a user’s information
ed. In response to the displayed
sults of the search engine, users click on
e links of the result page as they expect
e answer to be of relevance.
is clickdata can be represented as a folksonomy
which queries are descriptions of
icked URLs. The resulting network structure,
ich we will term logsonomy is very
milar to the one of folksonomies. In order
find out about its properties, we analyze
e topological characteristics of the tripartite
pergraph of queries, users and bookmarks
a large snapshot of del.icio.us and
query logs of two large search engines.
l of the three datasets show small world
operties. The tagging behavior of users,
ich is explained by preferential attachment
the tags in social bookmark systems, is
flected in the distribution of single query
rds in search engines. We can conclude
at the clicking behaviour of search engine
ers based on the displayed search results
d the tagging behaviour of social bookmarking
ers is driven by similar dynamics.

@inproceedings{krause2008logsonomy, author = {Krause, Beate and Jäschke, Robert and Hotho, Andreas and Stumme, Gerd}, title = {Logsonomy - Social Information Retrieval with Logdata}, booktitle = {HT '08: Proceedings of the Nineteenth ACM Conference on Hypertext and Hypermedia}, publisher = {ACM}, address = {New York, NY, USA}, year = {2008}, pages = {157--166}, url = {http://portal.acm.org/citation.cfm?id=1379092.1379123&coll=ACM&dl=ACM&type=series&idx=SERIES399&part=series&WantType=Journals&title=Proceedings%20of%20the%20nineteenth%20ACM%20conference%20on%20Hypertext%20and%20hypermedia}, doi = {http://doi.acm.org/10.1145/1379092.1379123}, isbn = {978-1-59593-985-2}, keywords = {analysis, folksonomy, log, network, search}, abstract = {Social bookmarking systems constitute an established
rt of the Web 2.0. In such systems
ers describe bookmarks by keywords
lled tags. The structure behind these social
stems, called folksonomies, can be viewed
a tripartite hypergraph of user, tag and resource
des. This underlying network shows
ecific structural properties that explain its
owth and the possibility of serendipitous
ploration.
day’s search engines represent the gateway
retrieve information from the World Wide
b. Short queries typically consisting of
o to three words describe a user’s information
ed. In response to the displayed
sults of the search engine, users click on
e links of the result page as they expect
e answer to be of relevance.
is clickdata can be represented as a folksonomy
which queries are descriptions of
icked URLs. The resulting network structure,
ich we will term logsonomy is very
milar to the one of folksonomies. In order
find out about its properties, we analyze
e topological characteristics of the tripartite
pergraph of queries, users and bookmarks
a large snapshot of del.icio.us and
query logs of two large search engines.
l of the three datasets show small world
operties. The tagging behavior of users,
ich is explained by preferential attachment
the tags in social bookmark systems, is
flected in the distribution of single query
rds in search engines. We can conclude
at the clicking behaviour of search engine
ers based on the displayed search results
d the tagging behaviour of social bookmarking
ers is driven by similar dynamics.} }

%0 = inproceedings %A = Krause, Beate and Jäschke, Robert and Hotho, Andreas and Stumme, Gerd %B = HT '08: Proceedings of the Nineteenth ACM Conference on Hypertext and Hypermedia %C = New York, NY, USA %D = 2008 %I = ACM %T = Logsonomy - Social Information Retrieval with Logdata %U = http://portal.acm.org/citation.cfm?id=1379092.1379123&coll=ACM&dl=ACM&type=series&idx=SERIES399&part=series&WantType=Journals&title=Proceedings%20of%20the%20nineteenth%20ACM%20conference%20on%20Hypertext%20and%20hypermedia

@article{leskovec2008planetary, author = {Leskovec, J. and Horvitz, E.}, title = {Planetary-scale views on a large instant-messaging network}, publisher = {ACM New York, NY, USA}, year = {2008}, url = {http://scholar.google.de/scholar.bib?q=info:dvYmn_qj6NQJ:scholar.google.com/&output=citation&hl=de&as_sdt=2000&ct=citation&cd=0}, keywords = {analysis, instant, messenger, network, small, ur, world} }

%0 = article %A = Leskovec, J. and Horvitz, E. %D = 2008 %I = ACM New York, NY, USA %T = Planetary-scale views on a large instant-messaging network %U = http://scholar.google.de/scholar.bib?q=info:dvYmn_qj6NQJ:scholar.google.com/&output=citation&hl=de&as_sdt=2000&ct=citation&cd=0

We use the emergent field of complex networks to analyze the network of scientific collaborations between entities (universities, research organizations, industry related companies,...) which collaborate in the context of the so-called framework programme. We demonstrate here that it is a scale-free network with an accelerated growth, which implies that the creation of new collaborations is encouraged. Moreover, these collaborations possess hierarchical modularity. Likewise, we find that the information flow depends on the size of the participants but not on geographical constraints.

@article{Almendral2007675, author = {Almendral, Juan A. and Oliveira, J.G. and López, L. and Mendes, J.F.F. and Sanjuán, Miguel A.F.}, title = {The network of scientific collaborations within the European framework programme}, journal = {Physica A: Statistical Mechanics and its Applications}, year = {2007}, volume = {384}, number = {2}, pages = {675 - 683}, url = {http://www.sciencedirect.com/science/article/B6TVG-4NTJH10-4/2/b209f12299c9e1d367a8298e7d986215}, doi = {DOI: 10.1016/j.physa.2007.05.049}, issn = {0378-4371}, keywords = {analysis, network, social, toread, ur}, abstract = {We use the emergent field of complex networks to analyze the network of scientific collaborations between entities (universities, research organizations, industry related companies,...) which collaborate in the context of the so-called framework programme. We demonstrate here that it is a scale-free network with an accelerated growth, which implies that the creation of new collaborations is encouraged. Moreover, these collaborations possess hierarchical modularity. Likewise, we find that the information flow depends on the size of the participants but not on geographical constraints.} }

%0 = article %A = Almendral, Juan A. and Oliveira, J.G. and López, L. and Mendes, J.F.F. and Sanjuán, Miguel A.F. %D = 2007 %T = The network of scientific collaborations within the European framework programme %U = http://www.sciencedirect.com/science/article/B6TVG-4NTJH10-4/2/b209f12299c9e1d367a8298e7d986215

Several algorithms have been proposed to compute partitions of networks
nto communities that score high on a graph clustering index called
odularity. While publications on these algorithms typically contain
xperimental evaluations to emphasize the plausibility of results,
one of these algorithms has been shown to actually compute optimal
artitions. We here settle the unknown complexity status of modularity
aximization by showing that the corresponding decision version is
P-complete in the strong sense. As a consequence, any efficient,
.e. polynomial-time, algorithm is only heuristic and yields suboptimal
artitions on many instances.

@misc{Brandes2006, author = {Brandes, U. and Delling, D. and Gaertler, M. and Goerke, R. and Hoefer, M. and Nikoloski, Z. and Wagner, D.}, title = {Maximizing Modularity is hard}, year = {2006}, note = {cite arxiv:physics/0608255 Comment: 10 pages, 1 figure}, url = {http://arxiv.org/abs/physics/0608255}, keywords = {community, complexity, detection, modularity, network, npc}, abstract = {Several algorithms have been proposed to compute partitions of networks into communities that score high on a graph clustering index called modularity. While publications on these algorithms typically contain experimental evaluations to emphasize the plausibility of results, none of these algorithms has been shown to actually compute optimal partitions. We here settle the unknown complexity status of modularity maximization by showing that the corresponding decision version is NP-complete in the strong sense. As a consequence, any efficient, i.e. polynomial-time, algorithm is only heuristic and yields suboptimal partitions on many instances.} }

%0 = misc %A = Brandes, U. and Delling, D. and Gaertler, M. and Goerke, R. and Hoefer, M. and Nikoloski, Z. and Wagner, D. %B = } %C = %D = 2006 %I = %T = Maximizing Modularity is hard} %U = http://arxiv.org/abs/physics/0608255

A key argument for modeling knowledge in ontologies is the easy re-use and re-engineering of the knowledge. However, current ontology engineering tools provide only basic functionalities for analyzing ontologies. Since ontologies can be considered as graphs, graph analysis techniques are a suitable answer for this need. Graph analysis has been performed by sociologists for over 60 years, and resulted in the vivid research area of Social Network Analysis (SNA). While social network structures currently receive high attention in the Semantic Web community, there are only very
ew SNA applications, and virtually none for analyzing the
tructure of ontologies.
We illustrate the benefits of applying SNA to ontologies and the Semantic Web, and discuss which research topics arise on the edge between the two areas. In particular, we discuss how different notions of centrality describe the core content and structure of an ontology. From the rather simple notion of degree centrality over betweenness centrality to the more complex eigenvector centrality, we illustrate the insights these measures provide on two ontologies, which are different in purpose, scope, and size.

@inproceedings{hoser2006semantic, author = {Hoser, Bettina and Hotho, Andreas and Jäschke, Robert and Schmitz, Christoph and Stumme, Gerd}, title = {Semantic Network Analysis of Ontologies}, booktitle = {The Semantic Web: Research and Applications}, series = {Lecture Notes in Computer Science}, publisher = {Springer}, year = {2006}, note = {Proceedings of the 3rd European Semantic Web Conference, Budva, Montenegro}, keywords = {analysis, network, ontology, semantic}, abstract = {A key argument for modeling knowledge in ontologies is the easy re-use and re-engineering of the knowledge. However, current ontology engineering tools provide only basic functionalities for analyzing ontologies. Since ontologies can be considered as graphs, graph analysis techniques are a suitable answer for this need. Graph analysis has been performed by sociologists for over 60 years, and resulted in the vivid research area of Social Network Analysis (SNA). While social network structures currently receive high attention in the Semantic Web community, there are only very
ew SNA applications, and virtually none for analyzing the
tructure of ontologies.
We illustrate the benefits of applying SNA to ontologies and the Semantic Web, and discuss which research topics arise on the edge between the two areas. In particular, we discuss how different notions of centrality describe the core content and structure of an ontology. From the rather simple notion of degree centrality over betweenness centrality to the more complex eigenvector centrality, we illustrate the insights these measures provide on two ontologies, which are different in purpose, scope, and size.} }

%0 = inproceedings %A = Hoser, Bettina and Hotho, Andreas and Jäschke, Robert and Schmitz, Christoph and Stumme, Gerd %B = The Semantic Web: Research and Applications %D = 2006 %I = Springer %T = Semantic Network Analysis of Ontologies

In this paper, we consider the evolution of structure within large online social networks. We present a series of measurements of two such networks, together comprising in excess of five million people and ten million friendship links, annotated with metadata capturing the time of every event in the life of the network. Our measurements expose a surprising segmentation of these networks into three regions: singletons who do not participate in the network; isolated communities which overwhelmingly display star structure; and a giant component anchored by a well-connected core region which persists even in the absence of stars.We present a simple model of network growth which captures these aspects of component structure. The model follows our experimental results, characterizing users as either passive members of the network; inviters who encourage offline friends and acquaintances to migrate online; and linkers who fully participate in the social evolution of the network.

@inproceedings{1150476, author = {Kumar, Ravi and Novak, Jasmine and Tomkins, Andrew}, title = {Structure and evolution of online social networks}, booktitle = {KDD '06: Proceedings of the 12th ACM SIGKDD international conference on Knowledge discovery and data mining}, publisher = {ACM}, address = {New York, NY, USA}, year = {2006}, pages = {611--617}, url = {http://portal.acm.org/citation.cfm?id=1150402.1150476}, doi = {http://doi.acm.org/10.1145/1150402.1150476}, isbn = {1-59593-339-5}, keywords = {analysis, link, network, sna, structure, toread, ur, user}, abstract = {In this paper, we consider the evolution of structure within large online social networks. We present a series of measurements of two such networks, together comprising in excess of five million people and ten million friendship links, annotated with metadata capturing the time of every event in the life of the network. Our measurements expose a surprising segmentation of these networks into three regions: singletons who do not participate in the network; isolated communities which overwhelmingly display star structure; and a giant component anchored by a well-connected core region which persists even in the absence of stars.We present a simple model of network growth which captures these aspects of component structure. The model follows our experimental results, characterizing users as either passive members of the network; inviters who encourage offline friends and acquaintances to migrate online; and linkers who fully participate in the social evolution of the network.} }

%0 = inproceedings %A = Kumar, Ravi and Novak, Jasmine and Tomkins, Andrew %B = KDD '06: Proceedings of the 12th ACM SIGKDD international conference on Knowledge discovery and data mining %C = New York, NY, USA %D = 2006 %I = ACM %T = Structure and evolution of online social networks %U = http://portal.acm.org/citation.cfm?id=1150402.1150476

@article{li2005towards, author = {Li, L. and Alderson, D. and Doyle, J.C. and Willinger, W.}, title = {Towards a theory of scale-free graphs: Definition, properties, and implications}, journal = {Internet Mathematics}, publisher = {AK Peters}, year = {2005}, volume = {2}, number = {4}, pages = {431--523}, url = {http://scholar.google.de/scholar.bib?q=info:Xi5NYPJyMvMJ:scholar.google.com/&output=citation&hl=de&as_sdt=2000&ct=citation&cd=0}, keywords = {free, law, network, power, scale, theory, ur} }

%0 = article %A = Li, L. and Alderson, D. and Doyle, J.C. and Willinger, W. %D = 2005 %I = AK Peters %T = Towards a theory of scale-free graphs: Definition, properties, and implications %U = http://scholar.google.de/scholar.bib?q=info:Xi5NYPJyMvMJ:scholar.google.com/&output=citation&hl=de&as_sdt=2000&ct=citation&cd=0

@article{menczer2004lexical, author = {Menczer, F.}, title = {Lexical and semantic clustering by web links}, journal = {Journal of the American Society for Information Science and Technology}, publisher = {Citeseer}, year = {2004}, volume = {55}, number = {14}, pages = {1261--1269}, url = {http://scholar.google.de/scholar.bib?q=info:qmPuziT0_h0J:scholar.google.com/&output=citation&hl=de&as_sdt=2000&ct=citation&cd=0}, keywords = {link, locality, network, structure, topical, toread, ur} }

%0 = article %A = Menczer, F. %D = 2004 %I = Citeseer %T = Lexical and semantic clustering by web links %U = http://scholar.google.de/scholar.bib?q=info:qmPuziT0_h0J:scholar.google.com/&output=citation&hl=de&as_sdt=2000&ct=citation&cd=0

@article{New03, author = {Newman, M. E. J.}, title = {The structure and function of complex networks}, journal = {SIAM Review}, year = {2003}, volume = {45}, number = {2}, pages = {167-256}, keywords = {graph, introduction, network, review, survey, theory} }

%0 = article %A = Newman, M. E. J. %D = 2003 %T = The structure and function of complex networks

We argue that social networks differ from most other types of networks, including technological and biological networks, in two important ways. First, they have nontrivial clustering or network transitivity and second, they show positive correlations, also called assortative mixing, between the degrees of adjacent vertices. Social networks are often divided into groups or communities, and it has recently been suggested that this division could account for the observed clustering. We demonstrate that group structure in networks can also account for degree correlations. We show using a simple model that we should expect assortative mixing in such networks whenever there is variation in the sizes of the groups and that the predicted level of assortative mixing compares well with that observed in real-world networks.

@article{NewmanPark2003aa, author = {Newman, M. E. J. and Park, Juyong}, title = {Why Social Networks Are Different from Other Types of Networks}, journal = {Phys. Rev. E}, year = {2003}, volume = {68}, number = {3}, pages = {036122}, keywords = {analysis, network, sna}, abstract = {We argue that social networks differ from most other types of networks, including technological and biological networks, in two important ways. First, they have nontrivial clustering or network transitivity and second, they show positive correlations, also called assortative mixing, between the degrees of adjacent vertices. Social networks are often divided into groups or communities, and it has recently been suggested that this division could account for the observed clustering. We demonstrate that group structure in networks can also account for degree correlations. We show using a simple model that we should expect assortative mixing in such networks whenever there is variation in the sizes of the groups and that the predicted level of assortative mixing compares well with that observed in real-world networks.} }

%0 = article %A = Newman, M. E. J. and Park, Juyong %D = 2003 %T = Why Social Networks Are Different from Other Types of Networks

@article{GirNew02, author = {Girvan, M. and Newman, M. E. J.}, title = {Community structure in social and biological networks}, journal = {PNAS}, year = {2002}, volume = {99}, number = {12}, pages = {7821-7826}, keywords = {community, detection, modularity, network} }

%0 = article %A = Girvan, M. and Newman, M. E. J. %D = 2002 %T = Community structure in social and biological networks

@article{newman02, author = {Newman, M. E. J. and Forrest, Stephanie and Balthrop, Justin}, title = {Email networks and the spread of computer viruses}, journal = {Phys. Rev. E}, publisher = {American Physical Society}, year = {2002}, volume = {66}, number = {3}, pages = {035101}, doi = {10.1103/PhysRevE.66.035101}, keywords = {degree, network, social} }

%0 = article %A = Newman, M. E. J. and Forrest, Stephanie and Balthrop, Justin %D = 2002 %I = American Physical Society %T = Email networks and the spread of computer viruses

@article{vázquez2002large, author = {Vázquez, A. and Pastor-Satorras, R. and Vespignani, A.}, title = {Large-scale topological and dynamical properties of the Internet}, journal = {Physical Review E}, publisher = {APS}, year = {2002}, volume = {65}, number = {6}, pages = {66130}, url = {http://scholar.google.de/scholar.bib?q=info:sEuhI6oKjFoJ:scholar.google.com/&output=citation&hl=de&as_sdt=2000&ct=citation&cd=0}, keywords = {analysis, correlation, degree, function, link, network} }

%0 = article %A = Vázquez, A. and Pastor-Satorras, R. and Vespignani, A. %D = 2002 %I = APS %T = Large-scale topological and dynamical properties of the Internet %U = http://scholar.google.de/scholar.bib?q=info:sEuhI6oKjFoJ:scholar.google.com/&output=citation&hl=de&as_sdt=2000&ct=citation&cd=0

@article{pastor2001dynamical, author = {Pastor-Satorras, R. and Vázquez, A. and Vespignani, A.}, title = {Dynamical and correlation properties of the Internet}, journal = {Physical Review Letters}, publisher = {APS}, year = {2001}, volume = {87}, number = {25}, pages = {258701}, url = {http://scholar.google.de/scholar.bib?q=info:KLiz1q2axUQJ:scholar.google.com/&output=citation&hl=de&as_sdt=2000&ct=citation&cd=0}, keywords = {analysis, correlation, degree, network} }

%0 = article %A = Pastor-Satorras, R. and Vázquez, A. and Vespignani, A. %D = 2001 %I = APS %T = Dynamical and correlation properties of the Internet %U = http://scholar.google.de/scholar.bib?q=info:KLiz1q2axUQJ:scholar.google.com/&output=citation&hl=de&as_sdt=2000&ct=citation&cd=0

@article{AmaScaBarSta00, author = {Amaral, L. A. N. and Scala, A. and Barthélémy, M. and Stanley, H. E.}, title = {Classes of small-world networks}, journal = {PNAS}, year = {2000}, volume = {97}, number = {21}, keywords = {network, properties, statistics} }

%0 = article %A = Amaral, L. A. N. and Scala, A. and Barthélémy, M. and Stanley, H. E. %D = 2000 %T = Classes of small-world networks

@article{broder2000graph, author = {Broder, A. and Kumar, R. and Maghoul, F. and Raghavan, P. and Rajagopalan, S. and Stata, R. and Tomkins, A. and Wiener, J.}, title = {Graph structure in the web}, journal = {Computer Networks}, publisher = {Elsevier}, year = {2000}, volume = {33}, number = {1-6}, pages = {309--320}, url = {http://scholar.google.de/scholar.bib?q=info:XK3rB5QCtqgJ:scholar.google.com/&output=citation&hl=de&as_sdt=2000&ct=citation&cd=0}, keywords = {analysis, network, scc, structure, toread, ur, web} }

%0 = article %A = Broder, A. and Kumar, R. and Maghoul, F. and Raghavan, P. and Rajagopalan, S. and Stata, R. and Tomkins, A. and Wiener, J. %D = 2000 %I = Elsevier %T = Graph structure in the web %U = http://scholar.google.de/scholar.bib?q=info:XK3rB5QCtqgJ:scholar.google.com/&output=citation&hl=de&as_sdt=2000&ct=citation&cd=0

Systems as diverse as genetic networks or the World Wide Web are best described as networks with complex topology. A common property of many large networks is that the vertex connectivities follow a scale-free power-law distribution. This feature was found to be a consequence of two generic mechanisms: (i) networks expand continuously by the addition of new vertices, and (ii) new vertices attach preferentially to sites that are already well connected. A model based on these two ingredients reproduces the observed stationary scale-free distributions, which indicates that the development of large networks is governed by robust self-organizing phenomena that go beyond the particulars of the individual systems.

@article{citeulike:90557, author = {Barabasi, A. L. and Albert, R.}, title = {Emergence of scaling in random networks}, journal = {Science}, address = {Department of Physics, University of Notre Dame, Notre Dame, IN 46556, USA.}, year = {1999}, volume = {286}, number = {5439}, pages = {509--512}, url = {http://view.ncbi.nlm.nih.gov/pubmed/10521342}, issn = {0036-8075}, keywords = {graph, network, properties, statistics}, abstract = {Systems as diverse as genetic networks or the World Wide Web are best described as networks with complex topology. A common property of many large networks is that the vertex connectivities follow a scale-free power-law distribution. This feature was found to be a consequence of two generic mechanisms: (i) networks expand continuously by the addition of new vertices, and (ii) new vertices attach preferentially to sites that are already well connected. A model based on these two ingredients reproduces the observed stationary scale-free distributions, which indicates that the development of large networks is governed by robust self-organizing phenomena that go beyond the particulars of the individual systems.} }

%0 = article %A = Barabasi, A. L. and Albert, R. %C = Department of Physics, University of Notre Dame, Notre Dame, IN 46556, USA. %D = 1999 %T = Emergence of scaling in random networks %U = http://view.ncbi.nlm.nih.gov/pubmed/10521342

@article{erdos1959, author = {Erdős, Pal and Rényi, Alfréd}, title = {On Random Graphs}, journal = {Publications Mathematicae}, year = {1959}, volume = {6}, pages = {290}, keywords = {analysis, graphs, network, random} }

%0 = article %A = Erdős, Pal and Rényi, Alfréd %D = 1959 %T = On Random Graphs