TY - GEN
T1 - Adversarial perturbations to manipulate the perception of power and influence in networks
AU - Avram, Mihai Valentin
AU - Mishra, Shubhanshu
AU - Parulian, Nikolaus Nova
AU - Diesner, Jana
N1 - Funding Information:
ACKNOWLEDGMENTS Research reported in this paper was sponsored in part by the Army Research Laboratory under Cooperative Agreement W911NF-17-2-0196. The views and conclusions contained in this document are those of the authors and should not be interpreted as representing the official policies, either expressed or implied, of the Army Research Laboratory or the U.S. Government. The U.S. Government is authorized to reproduce and distribute reprints for Government purposes notwithstanding any copyright notation here on.
Publisher Copyright:
© 2019 Association for Computing Machinery.
PY - 2019/8/27
Y1 - 2019/8/27
N2 - Observed social networks are often considered as proxies for underlying social networks. The analysis of observed networks oftentimes involves the identification of influential nodes via various centrality metrics. Our work is motivated by recent research on the investigation and design of adversarial attacks on machine learning systems. We apply the concept of adversarial attacks to social networks by studying strategies by which an adversary can minimally perturb the observed network structure to achieve their target function of modifying the ranking of nodes according to centrality measures. This can represent the attempts of an adversary to boost or demote the degree to which others perceive them as influential or powerful. It also allows us to study the impact of adversarial attacks on targets and victims, and to design metrics and security measures that help to identify and mitigate adversarial network attacks. We conduct a series of experiments on synthetic network data to identify attacks that allow the adversarial node to achieve their objective with a single move. We test this approach on different common network topologies and for common centrality metrics. We find that there is a small set of moves that result in the adversary achieving their objective, and this set is smaller for decreasing centrality metrics than for increasing them. These results can help with assessing the robustness of centrality measures. The notion of changing social network data to yield adversarial outcomes has practical implications, e.g., for information diffusion on social media, influence and power dynamics in social systems, and improving network security.
AB - Observed social networks are often considered as proxies for underlying social networks. The analysis of observed networks oftentimes involves the identification of influential nodes via various centrality metrics. Our work is motivated by recent research on the investigation and design of adversarial attacks on machine learning systems. We apply the concept of adversarial attacks to social networks by studying strategies by which an adversary can minimally perturb the observed network structure to achieve their target function of modifying the ranking of nodes according to centrality measures. This can represent the attempts of an adversary to boost or demote the degree to which others perceive them as influential or powerful. It also allows us to study the impact of adversarial attacks on targets and victims, and to design metrics and security measures that help to identify and mitigate adversarial network attacks. We conduct a series of experiments on synthetic network data to identify attacks that allow the adversarial node to achieve their objective with a single move. We test this approach on different common network topologies and for common centrality metrics. We find that there is a small set of moves that result in the adversary achieving their objective, and this set is smaller for decreasing centrality metrics than for increasing them. These results can help with assessing the robustness of centrality measures. The notion of changing social network data to yield adversarial outcomes has practical implications, e.g., for information diffusion on social media, influence and power dynamics in social systems, and improving network security.
KW - Adversarial attacks
KW - Centrality measures
KW - Network robustness
KW - Social network analysis
UR - http://www.scopus.com/inward/record.url?scp=85078820610&partnerID=8YFLogxK
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U2 - 10.1145/3341161.3345026
DO - 10.1145/3341161.3345026
M3 - Conference contribution
AN - SCOPUS:85078820610
T3 - Proceedings of the 2019 IEEE/ACM International Conference on Advances in Social Networks Analysis and Mining, ASONAM 2019
SP - 986
EP - 994
BT - Proceedings of the 2019 IEEE/ACM International Conference on Advances in Social Networks Analysis and Mining, ASONAM 2019
A2 - Spezzano, Francesca
A2 - Chen, Wei
A2 - Xiao, Xiaokui
PB - Association for Computing Machinery, Inc
T2 - 11th IEEE/ACM International Conference on Advances in Social Networks Analysis and Mining, ASONAM 2019
Y2 - 27 August 2019 through 30 August 2019
ER -