hundred American colleges and universities at a single point in time, and we
examine the roles of user attributes - gender, class year, major, high school,
and residence - at these institutions. We investigate the influence of common
attributes at the dyad level in terms of assortativity coefficients and
regression models. We then examine larger-scale groupings by detecting
communities algorithmically and comparing them to network partitions based on
the user characteristics. We thereby compare the relative importances of
different characteristics at different institutions, finding for example that
common high school is more important to the social organization of large
institutions and that the importance of common major varies significantly
between institutions. Our calculations illustrate how microscopic and
macroscopic perspectives give complementary insights on the social organization
at universities and suggest future studies to investigate such phenomena
further.
ER - TY - JOUR AU - Lerman, Kristina T1 - Social Information Processing in Social News Aggregation JO - arXiv PY - 2007/01 VL - IS - SP - EP - UR - http://arxiv.org/abs/cs.CY/0703087 DO - KW - digg KW - dynamics KW - flickr KW - network KW - social KW - toread L1 - SN - N1 - March 2008 N1 - AB - The rise of the social media sites, such as blogs, wikis, Digg and Flickr among others, underscores the transformation of the Web to a participatory medium in which users are collaboratively creating, evaluating and distributing information. The innovations introduced by social media has lead to a new paradigm for interacting with information, what we call 'social information processing'. In this paper, we study how social news aggregator Digg exploits social information processing to solve the problems of document recommendation and rating. First, we show, by tracking stories over time, that social networks play an important role in document recommendation. The second contribution of this paper consists of two mathematical models. The first model describes how collaborative rating and promotion of stories emerges from the independent decisions made by many users. The second model describes how a user's influence, the number of promoted stories and the user's social network, changes in time. We find qualitative agreement between predictions of the model and user data gathered from Digg. ER - TY - CONF AU - Xu, Xiaowei AU - Yuruk, Nurcan AU - Feng, Zhidan AU - Schweiger, Thomas A. J. A2 - T1 - SCAN: a structural clustering algorithm for networks T2 - KDD '07: Proceedings of the 13th ACM SIGKDD international conference on Knowledge discovery and data mining PB - ACM C1 - New York, NY, USA PY - 2007/ CY - VL - IS - SP - 824 EP - 833 UR - http://portal.acm.org/citation.cfm?doid=1281192.1281280 DO - http://doi.acm.org/10.1145/1281192.1281280 KW - clustering KW - graph KW - network KW - toread L1 - SN - 978-1-59593-609-7 N1 - SCAN N1 - AB - ER - TY - JOUR AU - Walker, Dylan AU - Xie, Huafeng AU - Yan, Koon-Kiu AU - Maslov, Sergei T1 - Ranking scientific publications using a model of network traffic JO - Journal of Statistical Mechanics: Theory and Experiment PY - 2007/ VL - 2007 IS - 06 SP - EP - UR - http://stacks.iop.org/1742-5468/2007/i=06/a=P06010 DO - KW - network KW - paper KW - publication KW - ranking KW - scientific KW - toread L1 - SN - N1 - N1 - AB - To account for strong ageing characteristics of citation networks, we modify the PageRank algorithm by initially distributing random surfers exponentially with age, in favour of more recent publications. The output of this algorithm, which we call CiteRank, is interpreted as approximate traffic to individual publications in a simple model of how researchers find new information. We optimize parameters of our algorithm to achieve the best performance. The results are compared for two rather different citation networks: all American Physical Society publications between 1893 and 2003 and the set of high-energy physics theory (hep-th) preprints. Despite major differences between these two networks, we find that their optimal parameters for the CiteRank algorithm are remarkably similar. The advantages and performance of CiteRank over more conventional methods of ranking publications are discussed. ER - TY - GEN AU - Maslov, Sergei AU - Redner, S. A2 - T1 - Promise and Pitfalls of Extending Google's PageRank Algorithm to Citation Networks JO - PB - C1 - PY - 2009/ VL - IS - SP - EP - UR - http://arxiv.org/abs/0901.2640 DO - KW - citation KW - index KW - network KW - pagerank KW - ranking KW - toread L1 - N1 - Promise and Pitfalls of Extending Google's PageRank Algorithm to Citation Networks N1 - AB - We review our recent work on applying the Google PageRank algorithm to find scientific "gems" among all Physical Review publications, and its extension to CiteRank, to find currently popular research directions. These metrics provide a meaningful extension to traditionally-used importance measures, such as the number of citations and journal impact factor. We also point out some pitfalls of over-relying on quantitative metrics to evaluate scientific quality. ER - TY - CONF AU - Anagnostopoulos, Aris AU - Brova, George AU - Terzi, Evimaria A2 - T1 - Peer and Authority Pressure in Information-Propagation Models T2 - Proceedings of the ECML/PKDD 2011 PB - C1 - PY - 2011/ CY - VL - IS - SP - EP - UR - DO - KW - analysis KW - authority KW - bibsonomy KW - network KW - peer KW - toread L1 - SN - N1 - N1 - AB - ER - TY - JOUR AU - Heidemann, Julia T1 - Online Social Networks – Ein sozialer und technischer Überblick JO - Informatik-Spektrum PY - 2009/ VL - IS - SP - EP - UR - http://dx.doi.org/10.1007/s00287-009-0367-0 DO - KW - info2.0 KW - network KW - social KW - systems KW - toread L1 - SN - N1 - SpringerLink - Zeitschriftenbeitrag N1 - AB - Zusammenfassung Online Social Networks wie Xing.com oder Facebook.com gehören zu den am stärksten wachsenden Diensten im Internet. Im Jahr
2008 nutzten geschätzte 580 Mio. Menschen weltweit diese Angebote. Entsprechend schnell haben sich Online Social Networksinnerhalb weniger Jahre von einem Nischenphänomen zu einem weltweiten Medium der IT-gestützten Kommunikation entwickelt. Insbesondereaufgrund stark wachsender Mitgliederzahlen entfalten Online Social Networks eine erhebliche gesellschaftliche und wirtschaftlicheBedeutung. Vor diesem Hintergrund ist es Ziel dieses Beitrags, Begriff und Eigenschaften, Entstehung und Entwicklung sowieNutzenpotenziale und Herausforderungen von Online Social Networks näher zu untersuchen. ER - TY - GEN AU - Kitsak, Maksim AU - Gallos, Lazaros K. AU - Havlin, Shlomo AU - Liljeros, Fredrik AU - Muchnik, Lev AU - Stanley, H. Eugene AU - Makse, Hernan A. A2 - T1 - Identifying influential spreaders in complex networks JO - PB - C1 - PY - 2010/ VL - IS - SP - EP - UR - http://arxiv.org/abs/1001.5285 DO - KW - analysis KW - centrality KW - network KW - social KW - toread L1 - N1 - Identifying influential spreaders in complex networks N1 - AB - Networks portray a multitude of interactions through which people meet, ideas
are spread, and infectious diseases propagate within a society. Identifying the
most efficient "spreaders" in a network is an important step to optimize the
use of available resources and ensure the more efficient spread of information.
Here we show that, in contrast to common belief, the most influential spreaders
in a social network do not correspond to the best connected people or to the
most central people (high betweenness centrality). Instead, we find: (i) The
most efficient spreaders are those located within the core of the network as
identified by the k-shell decomposition analysis. (ii) When multiple spreaders
are considered simultaneously, the distance between them becomes the crucial
parameter that determines the extend of the spreading. Furthermore, we find
that-- in the case of infections that do not confer immunity on recovered
individuals-- the infection persists in the high k-shell layers of the network
under conditions where hubs may not be able to preserve the infection. Our
analysis provides a plausible route for an optimal design of efficient
dissemination strategies.
ER - TY - JOUR AU - Paradiso, Joseph AU - Gips, Jonathan AU - Laibowitz, Mathew AU - Sadi, Sajid AU - Merrill, David AU - Aylward, Ryan AU - Maes, Pattie AU - Pentland, Alex T1 - Identifying and facilitating social interaction with a wearable wireless sensor network JO - Personal and Ubiquitous Computing PY - 2010/02 VL - 14 IS - 2 SP - 137 EP - 152 UR - http://dx.doi.org/10.1007/s00779-009-0239-2 DO - KW - network KW - rfid KW - toread L1 - SN - N1 - SpringerLink - Zeitschriftenbeitrag N1 - AB - Abstract We have designed a highly versatile badge system to facilitate a variety of interaction at large professional or social events
and serve as a platform for conducting research into human dynamics. The badges are equipped with a large LED display, wirelessinfrared and radio frequency networking, and a host of sensors to collect data that we have used to develop features and algorithmsaimed at classifying and predicting individual and group behavior. This paper overviews our badge system, describes the interactionsand capabilities that it enabled for the wearers, and presents data collected over several large deployments. This data isanalyzed to track and socially classify the attendees, predict their interest in other people and demonstration installations,profile the restlessness of a crowd in an auditorium, and otherwise track the evolution and dynamics of the events at whichthe badges were run. ER - TY - CONF AU - Backstrom, Lars AU - Huttenlocher, Daniel P. AU - Kleinberg, Jon M. AU - Lan, Xiangyang A2 - Eliassi-Rad, Tina A2 - Ungar, Lyle H. A2 - Craven, Mark A2 - Gunopulos, Dimitrios T1 - Group formation in large social networks: membership, growth, and evolution. T2 - KDD PB - ACM C1 - PY - 2006/ CY - VL - IS - SP - 44 EP - 54 UR - http://dblp.uni-trier.de/db/conf/kdd/kdd2006.html#BackstromHKL06 DO - KW - social KW - toread KW - evolution KW - community KW - network L1 - SN - 1-59593-339-5 N1 - dblp N1 - AB - ER - TY - CONF AU - Jin, YingZi AU - Matsuo, Yutaka AU - Ishizuka, Mitsuru A2 - Franconi, Enrico A2 - Kifer, Michael A2 - May, Wolfgang T1 - Extracting Social Networks among Various Entities on the Web T2 - Proceedings of the European Semantic Web Conference, ESWC2007 PB - Springer-Verlag C1 - PY - 2007/07 CY - VL - 4519 IS - SP - EP - UR - http://www.eswc2007.org/pdf/eswc07-jin.pdf DO - KW - extraction KW - network KW - relation KW - social KW - toread L1 - SN - N1 - N1 - AB - ER - TY - GEN AU - Hastings, M. B. A2 - T1 - Community Detection as an Inference Problem JO - PB - C1 - PY - 2006/04 VL - IS - SP - EP - UR - http://arxiv.org/abs/cond-mat/0604429 DO - KW - toread KW - community KW - network L1 - N1 - N1 - AB - We express community detection as an inference problem of determining the
most likely arrangement of communities. We then apply belief propagation and
mean-field theory to this problem, and show that this leads to fast, accurate
algorithms for community detection. ER - TY - JOUR AU - Hill, Shawndra AU - Agarwal, Deepak K. AU - Bell, Robert AU - Volinsky, Chris T1 - Building an Effective Representation for Dynamic Networks JO - Journal of Computational & Graphical Statistics PY - 2006/september
VL - 15 IS - SP - 584 EP - 608(25) UR - http://www.ingentaconnect.com/content/asa/jcgs/2006/00000015/00000003/art00006 DO - doi:10.1198/106186006X139162 KW - data KW - network KW - representation KW - toread L1 - SN - N1 - N1 - AB - A dynamic network is a special type of network composed of connected transactors which have repeated evolving interaction. Data on large dynamic networks such as telecommunications networks and the Internet are pervasive. However, representing dynamic networks in a manner that is conducive to efficient large-scale analysis is a challenge. In this article, we represent dynamic graphs using a data structure introduced in an earlier article. We advocate their representation because it accounts for the evolution of relationships between transactors through time, mitigates noise at the local transactor level, and allows for the removal of stale relationships. Our work improves on their heuristic arguments by formalizing the representation with three tunable parameters. In doing this, we develop a generic framework for evaluating and tuning any dynamic graph. We show that the storage saving approximations involved in the representation do not affect predictive performance, and typically improve it. We motivate our approach using a fraud detection example from the telecommunications industry, and demonstrate that we can outperform published results on the fraud detection task. In addition, we present a preliminary analysis on Web logs and e-mail networks.
ER - TY - GEN AU - McPherson, Miller AU - Smith-Lovin, Lynn AU - Cook, James M. A2 - T1 - Birds of a Feather: Homophily in Social Networks JO - Annual Review of Sociology PB - C1 - PY - 2001/ VL - 27 IS - SP - 415 EP - 444 UR - http://arjournals.annualreviews.org/doi/pdf/10.1146/annurev.soc.27.1.415 DO - KW - network KW - speed KW - toread L1 - N1 - N1 - AB - Similarity breeds connection. This principle-the homophily principle-structures network ties of every type, including marriage, friendship, work, advice, support, information transfer, exchange, comembership, and other types of relationship. The result is that people's personal networks are homogeneous with regard to many sociodemographic, behavioral, and intrapersonal characteristics. Homophily limits people's social worlds in a way that has powerful implications for the information they receive, the attitudes they form, and the interactions they experience. Homophily in race and ethnicity creates the strongest divides in our personal environments, with age, religion, education, occupation, and gender following in roughly that order. Geographic propinquity, families, organizations, and isomorphic positions in social systems all create contexts in which homophilous relations form. Ties between nonsimilar individuals also dissolve at a higher rate, which sets the stage for the formation of niches (localized positions) within social space. We argue for more research on: (a) the basic ecological processes that link organizations, associations, cultural communities, social movements, and many other social forms; (b) the impact of multiplex ties on the patterns of homophily; and (c) the dynamics of network change over time through which networks and other social entities co-evolve. ER - TY - GEN AU - Goldenberg, Anna AU - Zheng, Alice X AU - Fienberg, Stephen E AU - Airoldi, Edoardo M A2 - T1 - A survey of statistical network models JO - PB - C1 - PY - 2009/ VL - IS - SP - EP - UR - http://arxiv.org/abs/0912.5410 DO - KW - models KW - network KW - sota KW - survey KW - topic KW - toread L1 - N1 - A survey of statistical network models N1 - AB - Networks are ubiquitous in science and have become a focal point for
discussion in everyday life. Formal statistical models for the analysis of
network data have emerged as a major topic of interest in diverse areas of
study, and most of these involve a form of graphical representation.
Probability models on graphs date back to 1959. Along with empirical studies in
social psychology and sociology from the 1960s, these early works generated an
active network community and a substantial literature in the 1970s. This effort
moved into the statistical literature in the late 1970s and 1980s, and the past
decade has seen a burgeoning network literature in statistical physics and
computer science. The growth of the World Wide Web and the emergence of online
networking communities such as Facebook, MySpace, and LinkedIn, and a host of
more specialized professional network communities has intensified interest in
the study of networks and network data. Our goal in this review is to provide
the reader with an entry point to this burgeoning literature. We begin with an
overview of the historical development of statistical network modeling and then
we introduce a number of examples that have been studied in the network
literature. Our subsequent discussion focuses on a number of prominent static
and dynamic network models and their interconnections. We emphasize formal
model descriptions, and pay special attention to the interpretation of
parameters and their estimation. We end with a description of some open
problems and challenges for machine learning and statistics. ER - TY - GEN AU - White, Douglas R. AU - Kejzar, Natasa AU - Tsallis, Constantino AU - Farmer, Doyne AU - White, Scott A2 - T1 - A generative model for feedback networks JO - PB - C1 - PY - 2005/ VL - IS - SP - EP - UR - http://www.citebase.org/abstract?id=oai:arXiv.org:cond-mat/0508028 DO - KW - model KW - network KW - toread L1 - N1 - [cond-mat/0508028] A generative model for feedback networks N1 - AB - We investigate a simple generative model for network formation. The model is designed to describe the growth of networks of kinship, trading, corporate alliances, or autocatalytic chemical reactions, where feedback is an essential element of network growth. The underlying graphs in these situations grow via a competition between cycle formation and node addition. After choosing a given node, a search is made for another node at a suitable distance. If such a node is found, a link is added connecting this to the original node, and increasing the number of cycles in the graph; if such a node cannot be found, a new node is added, which is linked to the original node. We simulate this algorithm and find that we cannot reject the hypothesis that the empirical degree distribution is a q-exponential function, which has been used to model long-range processes in nonequilibrium statistical mechanics. ER -