1 research outputs found
Manipulating Node Similarity Measures in Networks
Node similarity measures quantify how similar a pair of nodes are in a
network. These similarity measures turn out to be an important fundamental tool
for many real world applications such as link prediction in networks,
recommender systems etc. An important class of similarity measures are local
similarity measures. Two nodes are considered similar under local similarity
measures if they have large overlap between their neighboring set of nodes.
Manipulating node similarity measures via removing edges is an important
problem. This type of manipulation, for example, hinders effectiveness of link
prediction in terrorists networks. Fortunately, all the popular computational
problems formulated around manipulating similarity measures turn out to be
NP-hard. We, in this paper, provide fine grained complexity results of these
problems through the lens of parameterized complexity. In particular, we show
that some of these problems are fixed parameter tractable (FPT) with respect to
various natural parameters whereas other problems remain intractable W[1]-hard
and W[2]-hard in particular). Finally we show the effectiveness of our proposed
FPT algorithms on real world datasets as well as synthetic networks generated
using Barabasi-Albert and Erdos-Renyi models.Comment: To appear as a full paper in AAMAS 202