Hiding in Temporal Networks

Abstract

Social network analysis tools can infer various attributes just by scrutinizing one's connections. Several researchers have studied the problem faced by an evader whose goal is to strategically rewire their social connections in order to mislead such tools, thereby concealing their private attributes. However, to date this literature has only considered static networks, while neglecting the more general case of temporal networks, where the structure evolves over time. Driven by this observation, we study how the evader can conceal their importance from an adversary armed with temporal centrality measures. We consider computational and structural aspects of this problem: Is it computationally feasible to calculate optimal ways of hiding? If it is, what network characteristics facilitate hiding? This topic has been studied in static networks, but in this work, we add realism to the problem by considering temporal networks of edges changing in time. We find that it is usually computationally infeasible to find the optimal way of hiding. On the other hand, by manipulating one's contacts, one could add a surprising amount of privacy. Compared to static networks, temporal networks offer more strategies for this type of manipulation and are thus, to some extent, easier to hide in.

Cyclical Evolution of Emerging Technology Innovation Network from a Temporal Network Perspective

“…Numerous real-world systems, such as those in nature, society, and technology, can be modeled as networks [54,79]. To date, most network studies have concentrated on static networks, while ignoring that many real networks undergo topological evolutions over time [79,80]. In particular, the topological evolution of emerging technology innovation networks, in reality, has obvious time attributes and will display temporal features with the cyclical development of emerging technology.…”

Social diffusion sources can escape detection

“…There exists a growing literature on avoiding detection by a wide range of social network analysis tools. Such hiding techniques can be used to prevent a closely cooperating group of nodes from being identified by community detection algorithms (Waniek et al, 2018), or prevent the leader of the organization from being recognized by centrality measures in standard (Waniek et al, 2017(Waniek et al, , 2021bWas et al, 2020), multilayer (Waniek et al, 2020), and temporal (Waniek et al, 2022) networks. Similar techniques can be used to prevent an undisclosed relationship from being exposed by link prediction algorithms (Waniek et al, 2019;Zhou et al, 2019).…”

Hiding From Centrality Measures: A Stackelberg Game Perspective

“…Another facet of the problem that was analyzed in the literature is the axiomatic characterization of the centrality measures that are resilient to manipulation [15]. While most works considers standard network structure, some examine networks that consist of multiple layers [16], or temporal networks where edges exists only at specific moments [17].…”