Abstract—There are myriad reallife examples of contagion
processes on human social networks, e.g., spread of viruses,
information, and social unrest. Also, there are many methods
to control or block contagion spread. In this work, we introduce
a novel method of blocking contagions that uses nodes from
dominating sets (DSs). To our knowledge, this is the first use
of DS nodes to block contagions. Finding minimum dominating
sets of graphs is an NPComplete problem, so we generalize a
wellknown heuristic, enabling us to customize its execution. Our
method produces a prioritized list of dominating nodes, which
is, in turn, a prioritized list of blocking nodes. Thus, for a given
network, we compute this list of blocking nodes and we use it to
block contagions for all blocking node budgets, contagion seed
sets, and parameter values of the contagion model. We report
on computational experiments of the blocking efficacy of our
approach using two mined networks. We also demonstrate the
effectiveness of our approach by comparing blocking results with
those from the high degree heuristic, which is a common standard
in blocking studies.
Index Terms—contagion blocking, dominating sets, threshold
models, social networks, simulation, high degree heuristic
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Using Dominating Sets to Block Contagions in Social Networks
There are myriad reallife examples of contagion processes on human social networks, e.g., spread of viruses, information, and social unrest. Also, there are many methods to control or block contagion spread. In this work, we introduce a novel method of blocking contagions that uses nodes from dominating sets (DSs). To our knowledge, this is the first use of DS nodes to block contagions. Finding minimum dominating sets of graphs is an NPComplete problem, so we generalize a wellknown heuristic, enabling us to customize its execution. Our method produces a prioritized list of dominating nodes, which is, in turn, a prioritized list of blocking nodes. Thus, for a given network, we compute this list of blocking nodes and we use it to block contagions for all blocking node budgets, contagion seed sets, and parameter values of the contagion model. We report on computational experiments of the blocking efficacy of our approach using two mined networks. We also demonstrate the effectiveness of our approach by comparing blocking results with those from the high degree heuristic, which is a common standard in blocking studies.
more »
« less
 Award ID(s):
 1916670
 NSFPAR ID:
 10385088
 Date Published:
 Journal Name:
 IEEE/ACM International Conference on Advances in Social Networks Analysis and Mining
 Page Range / eLocation ID:
 14
 Format(s):
 Medium: X
 Sponsoring Org:
 National Science Foundation
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