The Ripple Effect: Change in Trust and Its Impact Over a Social Network Export

@incollection{citeulike:6075266, abstract = {Computing trust between individuals in social networks is important for many intelligent systems that take advantage of reasoning in social situations. There have been many algorithms developed for inferring trust relationships in a variety of ways. These algorithms all work on a snapshot of the network; that is, they do not take into account changes in trust values over time. However, trust between people is always changing in realistic social networks and when changes happen, inferred trust values in the network will also change. Under these circumstances, the behavior the existing trust-inference algorithms is not yet very well understood. In this paper, we present an experimental study of several types of trust inference algorithms to answer the following questions on trust and change: • How far does a single change propagate through the network? • How large is the impact of that change? • How does this relate to the type of inference algorithm? Our experimental results provide insights into which algorithms are most suitable for certain applications.}, author = {Golbeck, Jennifer and Kuter, Ugur}, citeulike-article-id = {6075266}, citeulike-linkout-0 = {http://dx.doi.org/10.1007/978-1-84800-356-9_7}, citeulike-linkout-1 = {http://www.springerlink.com/content/t77w320627223663}, doi = {10.1007/978-1-84800-356-9_7}, journal = {Computing with Social Trust}, keywords = {activation, change, evolution, impact, network, ripple\_effect, social, spreading, trust}, pages = {169--181}, posted-at = {2009-11-05 16:47:00}, priority = {2}, title = {The Ripple Effect: Change in Trust and Its Impact Over a Social Network}, url = {http://dx.doi.org/10.1007/978-1-84800-356-9_7}, year = {2009} }

TY - CHAP ID - citeulike:6075266 L3 - citeulike-article-id:6075266 N2 - Computing trust between individuals in social networks is important for many intelligent systems that take advantage of reasoning in social situations. There have been many algorithms developed for inferring trust relationships in a variety of ways. These algorithms all work on a snapshot of the network; that is, they do not take into account changes in trust values over time. However, trust between people is always changing in realistic social networks and when changes happen, inferred trust values in the network will also change. Under these circumstances, the behavior the existing trust-inference algorithms is not yet very well understood. In this paper, we present an experimental study of several types of trust inference algorithms to answer the following questions on trust and change: • How far does a single change propagate through the network? • How large is the impact of that change? • How does this relate to the type of inference algorithm? Our experimental results provide insights into which algorithms are most suitable for certain applications. JF - Computing with Social Trust EP - 181 TI - The Ripple Effect: Change in Trust and Its Impact Over a Social Network SP - 169 KW - activation KW - change KW - evolution KW - impact KW - network KW - ripple_effect KW - social KW - spreading KW - trust AU - Golbeck, Jennifer AU - Kuter, Ugur PY - 2009/// UR - http://dx.doi.org/10.1007/978-1-84800-356-9_7 ER -