Influence and correlation in social networks. In: Proceeding of the 14th ACM SIGKDD International Conference on Knowledge Discovery and Data Mining. New York: ACM, 2008. 7-15A. Anagnostopoulos, R. Kumar, and M. Mahdian, "Influence and correlation in social networks," in KDD 2008....
Influence and correlation in social networks. In: Proceedings of the 14th ACM SIGKDD international conference on knowledge discovery and data mining. KDD ’08. New York: ACM; 2008. p. 7–15. doi:10.1145/1401890.1401897. Qasem Z, Jansen M, Hecking T, HoppeHU. On the detection of ...
Social influence occurs when one's opinions or behaviors are affected by others. It forms a prevalent, complex, and subtle force that governs the dynamics of social networks. With the rapid proliferation of online social networks such as Twitter, Facebook, Yelp, and Amazon, modeling the influenc...
Social network analysis (SNA) views social relationships in terms of network theory consisting of nodes and ties. Nodes are the individual actors within the networks; ties are the relationships between the actors. In the sequel, we will use the term node
Large-scale social platforms have enabled marketers to obtain rich data on the structure of word-of-mouth (WOM) networks and the correlation of friends' preferences (network assortativity). We study how the similarity or difference of friends' reservation prices for a product should affect the opt...
social network on a new platform, rather than establishing an entirely new social context; indeed, this appears to moderately hold at node-neighborhood level, as hinted by a Spearman correlation of 0.6 between the rankings of local clustering coefficients of shared nodes in Twitter and Mastodon ...
We present a generalised complex contagion model for describing behaviour and opinion spreading on social networks. Recurrent interactions between adjacent nodes and circular influence in loops in the network structure enable the modelling of influence spreading on the network scale. We have presented deta...
We measure node influence after t = 10 iterations of the IBMFA on some networks and verified that the value well represents the entropy values of the long-term dynamics of the system (see Fig. S11). The results of Fig. 4b indicate that there is no apparent correlation (Pearson’s ...
Research demonstrates the importance of prior knowledge and social networks for businesspeople who create new firms and markets (Shane, 2000, Hite and Hesterly, 2001, Wiklund and Shepherd, 2003). The first category—who entrepreneurs are—requires more attention, as it influences how they do ...
All analyses were performed in R version 4.0.349, using packages nlme50, multcomp51 and correlation52. Quantifying turn and speed influence To quantify individual influence from movement data, we define two complementary metrics, designed to serve as proxies for influence over group direction (turn ...