Adversarial social network analysis explores how social links can be altered or otherwise
manipulated to hinder unwanted information collection. To date, however, problems of this …

Signed graphs are well-suited for modeling social networks as they capture both positive
and negative relationships. Signed graph neural networks (SGNNs) are commonly …

Centrality measures can rank nodes in a social network according to their importance.
However, in many cases, a node may want to avoid being highly ranked by such measures …

Signed graphs are widely used to model the trust relationships among users in security-
sensitive systems such as cryptocurrency trading platforms, where trust prediction plays a …

Signed graphs consist of edges and signs, which can be separated into structural
information and balance-related information, respectively. Existing signed graph neural …

Attribute inference—the process of analyzing publicly available data in order to uncover
hidden information—has become a major threat to privacy, given the recent technological …

The Fairness and Goodness Algorithm (FGA) is a widely used trust system in signed
directed networks. However, attackers can manipulate trust scores on FGA by launching …

Signed social networks are widely used to model the trust relationships among online users
in security-sensitive systems such as cryptocurrency trading platforms, where trust prediction …

Social networks arise as a result of complex interactions among people, and homophily
plays an important role in this process. If we view homophily as a dominant force in network …

While the security of blockchains hinges on their underlying architecture, this thesis
emphasizes the necessity for a comprehensive analysis of various security aspects of …