@inproceedings{schneider2009understanding, abstract = {Online Social Networks (OSNs) have already attracted more than half a billion users. However, our understanding of which OSN features attract and keep the attention of these users is poor. Studies thus far have relied on surveys or interviews of OSN users or focused on static properties, e. g., the friendship graph, gathered via sampled crawls. In this paper, we study how users actually interact with OSNs by extracting clickstreams from passively monitored network traffic. Our characterization of user interactions within the OSN for four different OSNs (Facebook, LinkedIn, Hi5, and StudiVZ) focuses on feature popularity, session characteristics, and the dynamics within OSN sessions. We find, for example, that users commonly spend more than half an hour interacting with the OSNs while the byte contributions per OSN session are relatively small.}, acmid = {1644899}, address = {New York, NY, USA}, author = {Schneider, Fabian and Feldmann, Anja and Krishnamurthy, Balachander and Willinger, Walter}, booktitle = {Proceedings of the 9th ACM SIGCOMM Conference on Internet Measurement Conference}, doi = {10.1145/1644893.1644899}, interhash = {69b16bc81a34c976ea724b5e82ba2d8e}, intrahash = {f7ef42a9ba8edf63b6079a053d6bb9c6}, isbn = {978-1-60558-771-4}, location = {Chicago, Illinois, USA}, numpages = {14}, pages = {35--48}, publisher = {ACM}, series = {IMC '09}, title = {Understanding Online Social Network Usage from a Network Perspective}, url = {http://doi.acm.org/10.1145/1644893.1644899}, year = 2009 } @inproceedings{benevenuto2009characterizing, abstract = {Understanding how users behave when they connect to social networking sites creates opportunities for better interface design, richer studies of social interactions, and improved design of content distribution systems. In this paper, we present a first of a kind analysis of user workloads in online social networks. Our study is based on detailed clickstream data, collected over a 12-day period, summarizing HTTP sessions of 37,024 users who accessed four popular social networks: Orkut, MySpace, Hi5, and LinkedIn. The data were collected from a social network aggregator website in Brazil, which enables users to connect to multiple social networks with a single authentication. Our analysis of the clickstream data reveals key features of the social network workloads, such as how frequently people connect to social networks and for how long, as well as the types and sequences of activities that users conduct on these sites. Additionally, we crawled the social network topology of Orkut, so that we could analyze user interaction data in light of the social graph. Our data analysis suggests insights into how users interact with friends in Orkut, such as how frequently users visit their friends' or non-immediate friends' pages. In summary, our analysis demonstrates the power of using clickstream data in identifying patterns in social network workloads and social interactions. Our analysis shows that browsing, which cannot be inferred from crawling publicly available data, accounts for 92% of all user activities. Consequently, compared to using only crawled data, considering silent interactions like browsing friends' pages increases the measured level of interaction among users.}, acmid = {1644900}, address = {New York, NY, USA}, author = {Benevenuto, Fabr\'{\i}cio and Rodrigues, Tiago and Cha, Meeyoung and Almeida, Virg\'{\i}lio}, booktitle = {Proceedings of the 9th ACM SIGCOMM Conference on Internet Measurement Conference}, doi = {10.1145/1644893.1644900}, interhash = {ed9b10d4f36f90ddde9b95ce45b0b0be}, intrahash = {e5e25244e1ca2316a7871727e2df2bb9}, isbn = {978-1-60558-771-4}, location = {Chicago, Illinois, USA}, numpages = {14}, pages = {49--62}, publisher = {ACM}, series = {IMC '09}, title = {Characterizing User Behavior in Online Social Networks}, url = {http://doi.acm.org/10.1145/1644893.1644900}, year = 2009 } @article{jiang2013understanding, abstract = {Popular online social networks (OSNs) like Facebook and Twitter are changing the way users communicate and interact with the Internet. A deep understanding of user interactions in OSNs can provide important insights into questions of human social behavior and into the design of social platforms and applications. However, recent studies have shown that a majority of user interactions on OSNs are latent interactions, that is, passive actions, such as profile browsing, that cannot be observed by traditional measurement techniques. In this article, we seek a deeper understanding of both active and latent user interactions in OSNs. For quantifiable data on latent user interactions, we perform a detailed measurement study on Renren, the largest OSN in China with more than 220 million users to date. All friendship links in Renren are public, allowing us to exhaustively crawl a connected graph component of 42 million users and 1.66 billion social links in 2009. Renren also keeps detailed, publicly viewable visitor logs for each user profile. We capture detailed histories of profile visits over a period of 90 days for users in the Peking University Renren network and use statistics of profile visits to study issues of user profile popularity, reciprocity of profile visits, and the impact of content updates on user popularity. We find that latent interactions are much more prevalent and frequent than active events, are nonreciprocal in nature, and that profile popularity is correlated with page views of content rather than with quantity of content updates. Finally, we construct latent interaction graphs as models of user browsing behavior and compare their structural properties, evolution, community structure, and mixing times against those of both active interaction graphs and social graphs.}, acmid = {2517040}, address = {New York, NY, USA}, articleno = {18}, author = {Jiang, Jing and Wilson, Christo and Wang, Xiao and Sha, Wenpeng and Huang, Peng and Dai, Yafei and Zhao, Ben Y.}, doi = {10.1145/2517040}, interhash = {af18171c38a0b07fce62fb3fac5c6322}, intrahash = {aa9695f56135fd58de32b5b4a4c73698}, issn = {1559-1131}, issue_date = {October 2013}, journal = {ACM Trans. Web}, month = nov, number = 4, numpages = {39}, pages = {18:1--18:39}, publisher = {ACM}, title = {Understanding Latent Interactions in Online Social Networks}, url = {http://doi.acm.org/10.1145/2517040}, volume = 7, year = 2013 } @inproceedings{scholz2013people, author = {Scholz, Christoph and Atzmueller, Martin and Kibanov, Mark and Stumme, Gerd}, booktitle = {ASONAM}, interhash = {8b6051b794789000c4baa5ab059fab18}, intrahash = {bf958ff2b11df1d9d15d9287ea07a5c9}, title = {How Do People Link? Analysis of Contact Structures in Human Face-to-Face Proximity Networks}, year = 2013 } @incollection{atzmueller2013social, address = {Heidelberg, Germany}, author = {Atzmueller, Martin}, booktitle = {Mobile Social Networking: An Innovative Approach}, editor = {Chin, Alvin and Zhang, Daqing}, interhash = {1be75b604acbaf39653eeca9833782df}, intrahash = {cd910f3a16c9368e7b73407708452653}, publisher = {Springer Verlag}, title = {{Social Behavior in Mobile Social Networks: Characterizing Links, Roles and Communities}}, year = 2013 } @misc{ugander2011anatomy, abstract = {We study the structure of the social graph of active Facebook users, the largest social network ever analyzed. We compute numerous features of the graph including the number of users and friendships, the degree distribution, path lengths, clustering, and mixing patterns. Our results center around three main observations. First, we characterize the global structure of the graph, determining that the social network is nearly fully connected, with 99.91% of individuals belonging to a single large connected component, and we confirm the "six degrees of separation" phenomenon on a global scale. Second, by studying the average local clustering coefficient and degeneracy of graph neighborhoods, we show that while the Facebook graph as a whole is clearly sparse, the graph neighborhoods of users contain surprisingly dense structure. Third, we characterize the assortativity patterns present in the graph by studying the basic demographic and network properties of users. We observe clear degree assortativity and characterize the extent to which "your friends have more friends than you". Furthermore, we observe a strong effect of age on friendship preferences as well as a globally modular community structure driven by nationality, but we do not find any strong gender homophily. We compare our results with those from smaller social networks and find mostly, but not entirely, agreement on common structural network characteristics.}, author = {Ugander, Johan and Karrer, Brian and Backstrom, Lars and Marlow, Cameron}, interhash = {968abebf69b5959d2837eefcda3a8a32}, intrahash = {efad3d029704f09829373a443eeefdde}, note = {cite arxiv:1111.4503Comment: 17 pages, 9 figures, 1 table}, title = {The Anatomy of the Facebook Social Graph}, url = {http://arxiv.org/abs/1111.4503}, year = 2011 } @article{batagelj2011algorithms, abstract = {The structure of a large network (graph) can often be revealed by partitioning it into smaller and possibly more dense sub-networks that are easier to handle. One of such decompositions is based on “}, author = {Batagelj, Vladimir and Zaveršnik, Matjaž}, doi = {10.1007/s11634-010-0079-y}, interhash = {a0bd7331f81bb4da72ce115d5943d6e4}, intrahash = {cd0d5266688af6bb98bde7f99e3a54c1}, issn = {1862-5347}, journal = {Advances in Data Analysis and Classification}, language = {English}, number = 2, pages = {129--145}, publisher = {Springer}, title = {Fast algorithms for determining (generalized) core groups in social networks}, url = {http://dx.doi.org/10.1007/s11634-010-0079-y}, volume = 5, year = 2011 } @article{Atzmueller:12c, author = {Atzmueller, Martin}, interhash = {0b20c1d53d5df05326d594726273c2fb}, intrahash = {7b616e64994893a2aad95b5ad95db662}, journal = {WIREs: Data Mining and Knowledge Discovery}, title = {{Mining Social Media: Key Players, Sentiments, and Communities}}, volume = {In Press}, year = 2012 } @article{barabsi2013network, abstract = {Professor Barabási's talk described how the tools of network science can help understand the Web's structure, development and weaknesses. The Web is an information network, in which the nodes are documents (at the time of writing over one trillion of them), connected by links. Other well-known network structures include the Internet, a physical network where the nodes are routers and the links are physical connections, and organizations, where the nodes are people and the links represent communications.}, author = {Barabási, Albert-László}, doi = {10.1098/rsta.2012.0375}, eprint = {http://rsta.royalsocietypublishing.org/content/371/1987/20120375.full.pdf+html}, interhash = {e2cfdd2e3c7c68581e3ab691909ed28b}, intrahash = {208c1f9d6d8eff67cee07ebdf3cd0fc1}, journal = {Philosophical Transactions of the Royal Society A: Mathematical, Physical and Engineering Sciences}, number = 1987, title = {Network science}, url = {http://rsta.royalsocietypublishing.org/content/371/1987/20120375.abstract}, volume = 371, year = 2013 } @article{kleinberg2013analysis, abstract = {The growth of the Web has required us to think about the design of information systems in which large-scale computational and social feedback effects are simultaneously at work. At the same time, the data generated by Web-scale systems—recording the ways in which millions of participants create content, link information, form groups and communicate with one another—have made it possible to evaluate long-standing theories of social interaction, and to formulate new theories based on what we observe. These developments have created a new level of interaction between computing and the social sciences, enriching the perspectives of both of these disciplines. We discuss some of the observations, theories and conclusions that have grown from the study of Web-scale social interaction, focusing on issues including the mechanisms by which people join groups, the ways in which different groups are linked together in social networks and the interplay of positive and negative interactions in these networks.}, author = {Kleinberg, Jon}, doi = {10.1098/rsta.2012.0378}, eprint = {http://rsta.royalsocietypublishing.org/content/371/1987/20120378.full.pdf+html}, interhash = {b4686f01da53c975f342dbb40bdd1a90}, intrahash = {e3898cfb7206a7fee8eb3a5419aa030f}, journal = {Philosophical Transactions of the Royal Society A: Mathematical, Physical and Engineering Sciences}, month = mar, number = 1987, title = {Analysis of large-scale social and information networks}, url = {http://rsta.royalsocietypublishing.org/content/371/1987/20120378.abstract}, volume = 371, year = 2013 } @inproceedings{pfaltz2012entropy, abstract = {We introduce the concepts of closed sets and closure operators as mathematical tools for the study of social networks. Dynamic networks are represented by transformations. It is shown that under continuous change/transformation, all networks tend to "break down" and become less complex. It is a kind of entropy. The product of this theoretical decomposition is an abundance of triadically closed clusters which sociologists have observed in practice. This gives credence to the relevance of this kind of mathematical analysis in the sociological context. }, author = {Pfaltz, John L.}, booktitle = {Proceedings of the SOCINFO}, interhash = {753f13a5ffaa0946220164c2b05c230f}, intrahash = {044d0b1f6e737bede270a40bbddb0b06}, title = {Entropy in Social Networks}, year = 2012 } @article{birkholz2012scalable, abstract = {Studies on social networks have proved that endogenous and exogenous factors influence dynamics. Two streams of modeling exist on explaining the dynamics of social networks: 1) models predicting links through network properties, and 2) models considering the effects of social attributes. In this interdisciplinary study we work to overcome a number of computational limitations within these current models. We employ a mean-field model which allows for the construction of a population-specific socially informed model for predicting links from both network and social properties in large social networks. The model is tested on a population of conference coauthorship behavior, considering a number of parameters from available Web data. We address how large social networks can be modeled preserving both network and social parameters. We prove that the mean-field model, using a data-aware approach, allows us to overcome computational burdens and thus scalability issues in modeling large social networks in terms of both network and social parameters. Additionally, we confirm that large social networks evolve through both network and social-selection decisions; asserting that the dynamics of networks cannot singly be studied from a single perspective but must consider effects of social parameters. }, author = {Birkholz, Julie M. and Bakhshi, Rena and Harige, Ravindra and van Steen, Maarten and Groenewegen, Peter}, interhash = {a8ef0aac2eab74fc8eb3f9d3dc8a32dd}, intrahash = {aefcc2aa922b048bec85d5070494ed81}, journal = {CoRR}, month = sep, title = {Scalable Analysis of Socially Informed Network Models: the data-aware mean-field approach }, url = {http://arxiv.org/abs/1209.6615}, volume = {abs/1209.6615}, year = 2012 } @article{kautz1997referral, acmid = {245123}, address = {New York, NY, USA}, author = {Kautz, Henry and Selman, Bart and Shah, Mehul}, doi = {10.1145/245108.245123}, interhash = {6995678b936b33eef9ea1396e53a1fc7}, intrahash = {832d16a8c86e769c7ac9ace5381f757e}, issn = {0001-0782}, issue_date = {March 1997}, journal = {Communications of the ACM}, month = mar, number = 3, numpages = {3}, pages = {63--65}, publisher = {ACM}, title = {Referral Web: combining social networks and collaborative filtering}, url = {http://doi.acm.org/10.1145/245108.245123}, volume = 40, year = 1997 } @incollection{yu2000social, abstract = {Trust is important wherever agents must interact. We consider the important case of interactions in electronic communities, where the agents assist and represent principal entities, such as people and businesses. We propose a social mechanism of reputation management, which aims at avoiding interaction with undesirable participants. Social mechanisms complement hard security techniques (such as passwords and digital certificates), which only guarantee that a party is authenticated and authorized, but do not ensure that it exercises its authorization in a way that is desirable to others. Social mechanisms are even more important when trusted third parties are not available. Our specific approach to reputation management leads to a decentralized society in which agents help each other weed out undesirable players.}, address = {Berlin/Heidelberg}, affiliation = {Department of Computer Science, North Carolina State University, Raleigh, NC 27695-7534, USA}, author = {Yu, Bin and Singh, Munindar}, booktitle = {Cooperative Information Agents IV - The Future of Information Agents in Cyberspace}, doi = {10.1007/978-3-540-45012-2_15}, editor = {Klusch, Matthias and Kerschberg, Larry}, interhash = {1065a4963600ef4f9b4c034d3bbd9a50}, intrahash = {337afcb67138b927b27a9687199e8568}, isbn = {978-3-540-67703-1}, keyword = {Computer Science}, pages = {355--393}, publisher = {Springer}, series = {Lecture Notes in Computer Science}, title = {A Social Mechanism of Reputation Management in Electronic Communities}, url = {http://dx.doi.org/10.1007/978-3-540-45012-2_15}, volume = 1860, year = 2000 } @inproceedings{yu2003searching, abstract = {A referral system is a multiagent system whose member agents are capable of giving and following referrals. The specific cases of interest arise where each agent has a user. The agents cooperate by giving and taking referrals so each can better help its user locate relevant information. This use of referrals mimics human interactions and can potentially lead to greater effectiveness and efficiency than in single-agent systems.Existing approaches consider what referrals may be given and treat the referring process simply as path search in a static graph. By contrast, the present approach understands referrals as arising in and influencing dynamic social networks, where the agents act autonomously based on local knowledge. This paper studies strategies using which agents may search dynamic social networks. It evaluates the proposed approach empirically for a community of AI scientists (partially derived from bibliographic data). Further, it presents a prototype system that assists users in finding other users in practical social networks.}, acmid = {860587}, address = {New York, NY, USA}, author = {Yu, Bin and Singh, Munindar P.}, booktitle = {Proceedings of the second international joint conference on Autonomous agents and multiagent systems}, doi = {10.1145/860575.860587}, interhash = {1d5f1932e29ea02f82948d4efd12a0ad}, intrahash = {c6b422948459e04a86e766055608e55e}, isbn = {1-58113-683-8}, location = {Melbourne, Australia}, numpages = {8}, pages = {65--72}, publisher = {ACM}, title = {Searching social networks}, url = {http://doi.acm.org/10.1145/860575.860587}, year = 2003 } @article{pham2011development, abstract = {In contrast to many other scientific disciplines, computer science considers conference publications. Conferences have the advantage of providing fast publication of papers and of bringing researchers together to present and discuss the paper with peers. Previous work on knowledge mapping focused on the map of all sciences or a particular domain based on ISI published Journal Citation Report (JCR). Although this data cover most of the important journals, it lacks computer science conference and workshop proceedings, which results in an imprecise and incomplete analysis of the computer science knowledge. This paper presents an analysis on the computer science knowledge network constructed from all types of publications, aiming at providing a complete view of computer science research. Based on the combination of two important digital libraries (DBLP and CiteSeerX), we study the knowledge network created at journal/conference level using citation linkage, to identify the development of sub-disciplines. We investigate the collaborative and citation behavior of journals/conferences by analyzing the properties of their co-authorship and citation subgraphs. The paper draws several important conclusions. First, conferences constitute social structures that shape the computer science knowledge. Second, computer science is becoming more interdisciplinary. Third, experts are the key success factor for sustainability of journals/conferences.}, address = {Wien}, affiliation = {Information Systems and Database Technology, RWTH Aachen University, Aachen, Ahornstr. 55, 52056 Aachen, Germany}, author = {Pham, Manh and Klamma, Ralf and Jarke, Matthias}, doi = {10.1007/s13278-011-0024-x}, interhash = {193312234ed176aa8be9f35d4d1c4e72}, intrahash = {8ae08cacda75da80bfa5604cfce48449}, issn = {1869-5450}, journal = {Social Network Analysis and Mining}, keyword = {Computer Science}, number = 4, pages = {321--340}, publisher = {Springer}, title = {Development of computer science disciplines: a social network analysis approach}, url = {http://dx.doi.org/10.1007/s13278-011-0024-x}, volume = 1, year = 2011 } @article{pham2011development, abstract = {In contrast to many other scientific disciplines, computer science considers conference publications. Conferences have the advantage of providing fast publication of papers and of bringing researchers together to present and discuss the paper with peers. Previous work on knowledge mapping focused on the map of all sciences or a particular domain based on ISI published Journal Citation Report (JCR). Although this data cover most of the important journals, it lacks computer science conference and workshop proceedings, which results in an imprecise and incomplete analysis of the computer science knowledge. This paper presents an analysis on the computer science knowledge network constructed from all types of publications, aiming at providing a complete view of computer science research. Based on the combination of two important digital libraries (DBLP and CiteSeerX), we study the knowledge network created at journal/conference level using citation linkage, to identify the development of sub-disciplines. We investigate the collaborative and citation behavior of journals/conferences by analyzing the properties of their co-authorship and citation subgraphs. The paper draws several important conclusions. First, conferences constitute social structures that shape the computer science knowledge. Second, computer science is becoming more interdisciplinary. Third, experts are the key success factor for sustainability of journals/conferences.}, address = {Wien}, affiliation = {Information Systems and Database Technology, RWTH Aachen University, Aachen, Ahornstr. 55, 52056 Aachen, Germany}, author = {Pham, Manh and Klamma, Ralf and Jarke, Matthias}, doi = {10.1007/s13278-011-0024-x}, interhash = {193312234ed176aa8be9f35d4d1c4e72}, intrahash = {8ae08cacda75da80bfa5604cfce48449}, issn = {1869-5450}, journal = {Social Network Analysis and Mining}, keyword = {Computer Science}, number = 4, pages = {321--340}, publisher = {Springer}, title = {Development of computer science disciplines: a social network analysis approach}, url = {http://dx.doi.org/10.1007/s13278-011-0024-x}, volume = 1, year = 2011 } @article{Ross2009578, abstract = {Facebook is quickly becoming one of the most popular tools for social communication. However, Facebook is somewhat different from other Social Networking Sites as it demonstrates an offline-to-online trend; that is, the majority of Facebook Friends are met offline and then added later. The present research investigated how the Five-Factor Model of personality relates to Facebook use. Despite some expected trends regarding Extraversion and Openness to Experience, results indicated that personality factors were not as influential as previous literature would suggest. The results also indicated that a motivation to communicate was influential in terms of Facebook use. It is suggested that different motivations may be influential in the decision to use tools such as Facebook, especially when individual functions of Facebook are being considered.}, author = {Ross, Craig and Orr, Emily S. and Sisic, Mia and Arseneault, Jaime M. and Simmering, Mary G. and Orr, R. Robert}, doi = {10.1016/j.chb.2008.12.024}, interhash = {98f45e3865f7e4802258347ee91e678b}, intrahash = {fbcbb77a298da03d1e2d8bcd9ac3e0b4}, issn = {0747-5632}, journal = {Computers in Human Behavior}, note = {Including the Special Issue: State of the Art Research into Cognitive Load Theory}, number = 2, pages = {578 - 586}, title = {Personality and motivations associated with Facebook use}, url = {http://www.sciencedirect.com/science/article/pii/S0747563208002355}, volume = 25, year = 2009 } @incollection{koschtzki2005centrality, abstract = {Centrality indices are to quantify an intuitive feeling that in most networks some vertices or edges are more central than others. Many vertex centrality indices were introduced for the first time in the 1950s: e.g., the Bavelas index [50, 51], degree centrality [483] or a first feedback centrality, introduced by Seeley [510]. These early centralities raised a rush of research in which manifold applications were found. However, not every centrality index was suitable to every application, so with time, dozens of new centrality indices were published. This chapter will present some of the more influential, ‘classic’ centrality indices. We do not strive for completeness, but hope to give a catalog of basic centrality indices with some of their main applications.}, address = {Berlin / Heidelberg}, affiliation = {IPK Gatersleben, Corrensstraße 3, 06466 Gatersleben, Germany}, author = {Koschützki, Dirk and Lehmann, Katharina and Peeters, Leon and Richter, Stefan and Tenfelde-Podehl, Dagmar and Zlotowski, Oliver}, booktitle = {Network Analysis}, doi = {10.1007/978-3-540-31955-9_3}, editor = {Brandes, Ulrik and Erlebach, Thomas}, interhash = {8bfa60518049d9dbc7f6ce7b5c2914be}, intrahash = {567d2f61b08e78af53463b2a30729830}, isbn = {978-3-540-24979-5}, keyword = {Computer Science}, pages = {16-61}, publisher = {Springer}, series = {Lecture Notes in Computer Science}, title = {Centrality Indices}, url = {http://dx.doi.org/10.1007/978-3-540-31955-9_3}, volume = 3418, year = 2005 } @book{brandes2005network, address = {[New York]}, author = {Brandes, Ulrik. and Erlebach, Thomas.}, interhash = {ae40403faa9a80926c66da73cf6e29ba}, intrahash = {11695c81746f2ac6e25fab7c6ed49fbf}, isbn = {9783540249795 3540249796 9783540319559 3540319557}, publisher = {Springer-Verlag Berlin/Heidelberg}, refid = {318289062}, title = {Network Analysis}, url = {http://www.worldcat.org/search?qt=worldcat_org_all&q=3540249796}, year = 2005 }