• Corpus ID: 231986269

Social Diffusion Sources Can Escape Detection

@article{Waniek2021SocialDS,
  title={Social Diffusion Sources Can Escape Detection},
  author={Marcin Waniek and Manuel Cebrian and Petter Holme and Talal Rahwan},
  journal={ArXiv},
  year={2021},
  volume={abs/2102.10539}
}
Influencing (and being influenced by) others indirectly through social networks is fundamental to all human societies. Whether this happens through the diffusion of rumors, viruses, opinions, or knowhow, finding the source is of persistent interest to people and an algorithmic challenge of much current research interest. However, no study has considered the case of diffusion sources actively trying to avoid detection. By disregarding this assumption, we risk conflating intentional obfuscation… 
Hiding in Temporal Networks
TLDR
It is usually computationally infeasible to find the optimal way of hiding, but by manipulating one’s contacts, one could add a surprising amount of privacy by considering temporal networks of edges changing in time.
How Members of Covert Networks Conceal the Identities of Their Leaders
TLDR
This work analyzes the problem of choosing a set of edges to be added to a network to decrease the leaders’ ranking according to three fundamental centrality measures, namely, degree, closeness, and betweenness and proves that this problem is NP-complete for each measure.

References

SHOWING 1-10 OF 48 REFERENCES
Hiding individuals and communities in a social network
TLDR
It is shown that individuals and communities can disguise themselves from detection online by standard social network analysis tools through simple changes to their social network connections.
Source detection of rumor in social network - A review
An analysis of social network-based Sybil defenses
TLDR
It is demonstrated that networks with well-defined community structure are inherently more vulnerable to Sybil attacks, and that, in such networks, Sybils can carefully target their links in order to make their attacks more effective.
How to Hide One’s Relationships from Link Prediction Algorithms
TLDR
It is proved that the optimization problem faced by such an individual is NP-complete, meaning that any attempt to identify an optimal way to hide one’s relationships is futile, and effective heuristics that are readily-applicable by users of existing social media platforms to conceal any connections they deem sensitive are developed.
Fast rumor source identification via random walks
TLDR
This work proposes a heuristic based on the hitting time statistics of a surrogate random walk process that can be used to approximate the maximum likelihood estimator of the rumor source.
Aiding the Detection of Fake Accounts in Large Scale Social Online Services
TLDR
A new tool in the hands of OSN operators, which relies on social graph properties to rank users according to their perceived likelihood of being fake (SybilRank), which is computationally efficient and can scale to graphs with hundreds of millions of nodes, as demonstrated by the Hadoop prototype.
Rumors in a Network: Who's the Culprit?
TLDR
Simulations show that rumor centrality outperforms distance centrality in finding rumor sources in networks which are not tree-like, and it is proved that on trees, the rumor center and distance center are equivalent, but on general networks, they may differ.
Uncovering Large Groups of Active Malicious Accounts in Online Social Networks
TLDR
This work designs and implements a malicious account detection system called SynchroTrap that clusters user accounts according to the similarity of their actions and uncovers large groups of malicious accounts that act similarly at around the same time for a sustained period of time.
SybilGuard: Defending Against Sybil Attacks via Social Networks
TLDR
This paper presents SybilGuard, a novel protocol for limiting the corruptive influences of sybil attacks, based on the ldquosocial networkrdquo among user identities, where an edge between two identities indicates a human-established trust relationship.
The spread of low-credibility content by social bots
TLDR
It is found that bots play a major role in the spread of low-credibility content on Twitter, and control measures for limiting thespread of misinformation are suggested.
...
1
2
3
4
5
...