Temporal Distance Metrics for Social Network Analysis Export

@inproceedings{tang:metrics, abstract = {The analysis of social and technological networks has attracted a lot of attention as social networking applications and mobile sensing devices have given us a wealth of real data. Classic studies looked at analysing static or aggregated networks, i.e., networks that do not change over time or built as the results of aggregation of information over a certain period of time. Given the soaring collections of measurements related to very large, real network traces, researchers are quickly starting to realise that connections are inherently varying over time and exhibit more dimensionality than static analysis can capture.}, address = {New York, NY, USA}, author = {Tang, John and Musolesi, Mirco and Mascolo, Cecilia and Latora, Vito}, booktitle = {Proceedings of the Second ACM SIGCOMM Workshop on Online Social Networks}, citeulike-article-id = {5679596}, citeulike-linkout-0 = {http://portal.acm.org/citation.cfm?id=1592665.1592674}, citeulike-linkout-1 = {http://dx.doi.org/10.1145/1592665.1592674}, citeulike-linkout-2 = {http://conferences.sigcomm.org/sigcomm/2009/workshops/wosn/papers/p31.pdf}, doi = {10.1145/1592665.1592674}, isbn = {978-1-60558-445-4}, keywords = {cambridge\_haggle, crawdad, information-dissemination, opw, social-network-analysis}, location = {Barcelona, Spain}, month = {August}, pages = {31--36}, posted-at = {2009-08-29 19:46:03}, priority = {5}, publisher = {ACM}, title = {Temporal Distance Metrics for Social Network Analysis}, url = {http://dx.doi.org/10.1145/1592665.1592674}, year = {2009} }

TY - CONF ID - tang:metrics L3 - citeulike-article-id:5679596 N2 - The analysis of social and technological networks has attracted a lot of attention as social networking applications and mobile sensing devices have given us a wealth of real data. Classic studies looked at analysing static or aggregated networks, i.e., networks that do not change over time or built as the results of aggregation of information over a certain period of time. Given the soaring collections of measurements related to very large, real network traces, researchers are quickly starting to realise that connections are inherently varying over time and exhibit more dimensionality than static analysis can capture. CY - Barcelona, Spain SN - 978-1-60558-445-4 AD - New York, NY, USA EP - 36 TI - Temporal Distance Metrics for Social Network Analysis PB - ACM T2 - Proceedings of the Second ACM SIGCOMM Workshop on Online Social Networks SP - 31 KW - cambridge_haggle KW - crawdad KW - information-dissemination KW - opw KW - social-network-analysis AU - Tang, John AU - Musolesi, Mirco AU - Mascolo, Cecilia AU - Latora, Vito PY - 2009/August// UR - http://dx.doi.org/10.1145/1592665.1592674 ER -