att_abstract={{We study the problem of group linkage: linking records that refer to
multiple entities in the same group. Applications for group linkage
include finding businesses in the same chain, finding social network
users from the same organization, and so on. Group linkage faces
new challenges compared to traditional entity resolution. First, although
different members in the same group can share some similar
global values of an attribute, they represent different entities so can
also have distinct local values for the same or different attributes,
requiring a high tolerance for value diversity. Second, we need to
be able to distinguish local values from erroneous values.

We present a robust two-stage algorithm: the first stage identifies
pivots–maximal sets of records that are very likely to belong to
the same group, while being robust to possible erroneous values;
the second stage collects strong evidence from the pivots and leverages
it for merging more records into the same group, while being
tolerant to differences in local values of an attribute. Experimental
results show the high effectiveness and efficiency of our algorithm
on various real-world data sets.}},
	att_categories={C_BB.1, C_NSS.2, C_IIS.5},
	att_copyright={{International World Wide Web Conference Committee (IW3C2)}},
	att_copyright_notice={{The definitive version was published in 2015. {{, 2015-05-15}}
	author={Divesh Srivastava and Pei Li and Xin Luna Dong and Songtao Guo and Andrea Maurino},
	institution={{International World Wide Web Conference, 2015}},
	title={{Robust Group Linkage}},