Measuring tie strength in implicit social networks

Given a set of people and a set of events attended by them, we address the problem of measuring connectedness or tie strength between each pair of persons. The underlying assumption is that attendance at mutual events gives an implicit social network between people. We take an axiomatic approach to this problem. Starting from a list of axioms, which a measure of tie strength must satisfy, we characterize functions that satisfy all the axioms. We then show that there is a range of tie-strength measures that satisfy this characterization. A measure of tie strength induces a ranking on the edges of the social network (and on the set of neighbors for every person). We show that for applications where the ranking, and not the absolute value of the tie strength, is the important thing about the measure, the axioms are equivalent to a natural partial order. To settle on a particular measure, we must make a non-obvious decision about extending this partial order to a total order. This decision is best left to particular applications. We also classify existing tie-strength measures according to the axioms that they satisfy; and observe that none of the "self-referential" tie-strength measures satisfy the axioms. In our experiments, we demonstrate the efficacy of our approach; show the completeness and soundness of our axioms, and present Kendall Tau Rank Correlation between various tie-strength measures.

[1]  David Lazer,et al.  Inferring friendship network structure by using mobile phone data , 2009, Proceedings of the National Academy of Sciences.

[2]  Lada A. Adamic,et al.  Friends and neighbors on the Web , 2003, Soc. Networks.

[3]  Jennifer Widom,et al.  SimRank: a measure of structural-context similarity , 2002, KDD.

[4]  R. Bornstein Exposure and affect: Overview and meta-analysis of research, 1968–1987. , 1989 .

[5]  Jennifer Neville,et al.  Using Transactional Information to Predict Link Strength in Online Social Networks , 2009, ICWSM.

[6]  Ruoming Jin,et al.  Axiomatic ranking of network role similarity , 2011, KDD.

[7]  Leo Katz,et al.  A new status index derived from sociometric analysis , 1953 .

[8]  D. Lazer,et al.  Inferring Social Network Structure using Mobile Phone Data , 2006 .

[9]  Matthieu Latapy,et al.  Link prediction in bipartite graphs using internal links and weighted projection , 2011, 2011 IEEE Conference on Computer Communications Workshops (INFOCOM WKSHPS).

[10]  Mark S. Granovetter The Strength of Weak Ties , 1973, American Journal of Sociology.

[11]  R. Zajonc Attitudinal effects of mere exposure. , 1968 .

[12]  L. Khachiyan,et al.  On the conductance of order Markov chains , 1991 .

[13]  Eric Gilbert,et al.  Predicting tie strength with social media , 2009, CHI.

[14]  Moshe Tennenholtz,et al.  Ranking systems: the PageRank axioms , 2005, EC '05.

[15]  Yossi Matias,et al.  Suggesting friends using the implicit social graph , 2010, KDD.

[16]  Dekang Lin,et al.  An Information-Theoretic Definition of Similarity , 1998, ICML.

[17]  Jon M. Kleinberg,et al.  The link-prediction problem for social networks , 2007, J. Assoc. Inf. Sci. Technol..