  In social network analysis, automatic social circle detection in ego-networks
is becoming a fundamental and important task, with many potential applications
such as user privacy protection or interest group recommendation. So far, most
studies have focused on addressing two questions, namely, how to detect
overlapping circles and how to detect circles using a combination of network
structure and network node attributes. This paper asks an orthogonal research
question, that is, how to detect circles based on network structures that are
(usually) described by multiple views? Our investigation begins with crawling
ego-networks from Twitter and employing classic techniques to model their
structures by six views, including user relationships, user interactions and
user content. We then apply both standard and our modified multi-view spectral
clustering techniques to detect social circles in these ego-networks. Based on
extensive automatic and manual experimental evaluations, we deliver two major
findings: first, multi-view clustering techniques perform better than common
single-view clustering techniques, which only use one view or naively integrate
all views for detection, second, the standard multi-view clustering technique
is less robust than our modified technique, which selectively transfers
information across views based on an assumption that sparse network structures
are (potentially) incomplete. In particular, the second finding makes us
believe a direct application of standard clustering on potentially incomplete
networks may yield biased results. We lightly examine this issue in theory,
where we derive an upper bound for such bias by integrating theories of
spectral clustering and matrix perturbation, and discuss how it may be affected
by several network characteristics.
