Gupta, T.; Garg, S.; Mahanti, A.; Carlsson, N. & Arlitt, M. (2009), Characterization of FriendFeed - A Web-based Social Aggregation Service, in .
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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.

Blondel, V.; Guillaume, J.; Lambiotte, R. & Mech, E. (2008), 'Fast unfolding of communities in large networks', J. Stat. Mech , P10008 .
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Krause, B.; Jäschke, R.; Hotho, A. & Stumme, G. (2008), Logsonomy - Social Information Retrieval with Logdata, in 'HT '08: Proceedings of the Nineteenth ACM Conference on Hypertext and Hypermedia' , ACM, New York, NY, USA , pp. 157--166 .
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Social bookmarking systems constitute an established part of the Web 2.0. In such systems users describe bookmarks by keywords called tags. The structure behind these social systems, called folksonomies, can be viewed as a tripartite hypergraph of user, tag and resource nodes. This underlying network shows specific structural properties that explain its growth and the possibility of serendipitous exploration. Today’s search engines represent the gateway to retrieve information from the World Wide Web. Short queries typically consisting of two to three words describe a user’s information need. In response to the displayed results of the search engine, users click on the links of the result page as they expect the answer to be of relevance. This clickdata can be represented as a folksonomy in which queries are descriptions of clicked URLs. The resulting network structure, which we will term logsonomy is very similar to the one of folksonomies. In order to find out about its properties, we analyze the topological characteristics of the tripartite hypergraph of queries, users and bookmarks on a large snapshot of del.icio.us and on query logs of two large search engines. All of the three datasets show small world properties. The tagging behavior of users, which is explained by preferential attachment of the tags in social bookmark systems, is reflected in the distribution of single query words in search engines. We can conclude that the clicking behaviour of search engine users based on the displayed search results and the tagging behaviour of social bookmarking users is driven by similar dynamics.

2007

Almendral, J. A.; Oliveira, J.; López, L.; Mendes, J. & Sanjuán, M. A. (2007), 'The network of scientific collaborations within the European framework programme', Physica A: Statistical Mechanics and its Applications 384 (2) , 675 - 683 .
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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.

2006

Brandes, U.; Delling, D.; Gaertler, M.; Goerke, R.; Hoefer, M.; Nikoloski, Z. & Wagner, D. (2006), 'Maximizing Modularity is hard' , cite arxiv:physics/0608255 Comment: 10 pages, 1 figure .
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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.

Hoser, B.; Hotho, A.; Jäschke, R.; Schmitz, C. & Stumme, G. (2006), Semantic Network Analysis of Ontologies, in 'The Semantic Web: Research and Applications' , Springer, .
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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 few SNA applications, and virtually none for analyzing the structure 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.

Kumar, R.; Novak, J. & Tomkins, A. (2006), Structure and evolution of online social networks, in 'KDD '06: Proceedings of the 12th ACM SIGKDD international conference on Knowledge discovery and data mining' , ACM, New York, NY, USA , pp. 611--617 .
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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.

2005

Li, L.; Alderson, D.; Doyle, J. & Willinger, W. (2005), 'Towards a theory of scale-free graphs: Definition, properties, and implications', Internet Mathematics 2 (4) , 431--523 .
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2004

Menczer, F. (2004), 'Lexical and semantic clustering by web links', Journal of the American Society for Information Science and Technology 55 (14) , 1261--1269 .
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2003

Newman, M. E. J. & Park, J. (2003), 'Why Social Networks Are Different from Other Types of Networks', Phys. Rev. E 68 (3) , 036122 .
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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.

2002

Girvan, M. & Newman, M. E. J. (2002), 'Community structure in social and biological networks', PNAS 99 (12) , 7821-7826 .
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Newman, M. E. J.; Forrest, S. & Balthrop, J. (2002), 'Email networks and the spread of computer viruses', Phys. Rev. E 66 (3) , 035101 .
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Vázquez, A.; Pastor-Satorras, R. & Vespignani, A. (2002), 'Large-scale topological and dynamical properties of the Internet', Physical Review E 65 (6) , 66130 .
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2001

Pastor-Satorras, R.; Vázquez, A. & Vespignani, A. (2001), 'Dynamical and correlation properties of the Internet', Physical Review Letters 87 (25) , 258701 .
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2000

Amaral, L. A. N.; Scala, A.; Barthélémy, M. & Stanley, H. E. (2000), 'Classes of small-world networks', PNAS 97 (21) .
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Broder, A.; Kumar, R.; Maghoul, F.; Raghavan, P.; Rajagopalan, S.; Stata, R.; Tomkins, A. & Wiener, J. (2000), 'Graph structure in the web', Computer Networks 33 (1-6) , 309--320 .
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1999

Barabasi, A. L. & Albert, R. (1999), 'Emergence of scaling in random networks', Science 286 (5439) , 509--512 .
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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.