Tootfinder

This https://arxiv.org/abs/2306.04366 has been replaced.
initial toot: https://mastoxiv.page/@arXiv_csSI_…

Enhancing Worker Recruitment in Collaborative Mobile Crowdsourcing: A Graph Neural Network Trust Evaluation Approach
Collaborative Mobile Crowdsourcing (CMCS) allows platforms to recruit worker teams to collaboratively execute complex sensing tasks. The efficiency of such collaborations could be influenced by trust relationships among workers. To obtain the asymmetric trust values among all workers in the social network, the Trust Reinforcement Evaluation Framework (TREF) based on Graph Convolutional Neural Networks (GCNs) is proposed in this paper. The task completion effect is comprehensively calculated by …

This https://arxiv.org/abs/2306.04366 has been replaced.
initial toot: https://mastoxiv.page/@arXiv_csSI_…

Enhancing Worker Recruitment in Collaborative Mobile Crowdsourcing: A Graph Neural Network Trust Evaluation Approach
Collaborative Mobile Crowdsourcing (CMCS) allows platforms to recruit worker teams to collaboratively execute complex sensing tasks. The efficiency of such collaborations could be influenced by trust relationships among workers. To obtain the asymmetric trust values among all workers in the social network, the Trust Reinforcement Evaluation Framework (TREF) based on Graph Convolutional Neural Networks (GCNs) is proposed in this paper. The task completion effect is comprehensively calculated by …

star-map style DHTs
only keeping around a few key nodes at distances 1,2,4,&8
so like you'd only track the PGP keys / tor addresses of your direct friends, theirs, etc
would that survive rotating tor addresses (which might change more than rotating keys)?
what about key compromise rotations?
could you re-assemble your friend graph's real keys with enough of a "pulsar map"?
this isn't even assuming they're picking a few trusted NS nodes (that... might help things?) - just that you trust the constellation of near-friends in general to be honest-ish
#P2P #DHT #Libp2p

This https://arxiv.org/abs/2404.14246 has been replaced.
initial toot: https://mastoxiv.page/@arXiv_csCR_…

Chain of trust: Unraveling the references among Common Criteria certified products
With 5394 security certificates of IT products and systems, the Common Criteria for Information Technology Security Evaluation have bred an ecosystem entangled with various kind of relations between the certified products. Yet, the prevalence and nature of dependencies among Common Criteria certified products remains largely unexplored. This study devises a novel method for building the graph of references among the Common Criteria certified products, determining the different contexts of refer…

To Trust or Not to Trust: Assignment Mechanisms with Predictions in the Private Graph Model
Riccardo Colini-Baldeschi, Sophie Klumper, Guido Sch\"afer, Artem Tsikiridis
https://arxiv.org/abs/2403.03725

To Trust or Not to Trust: Assignment Mechanisms with Predictions in the Private Graph Model
The realm of algorithms with predictions has led to the development of several new algorithms that leverage (potentially erroneous) predictions to enhance their performance guarantees. The challenge is to devise algorithms that achieve optimal approximation guarantees as the prediction quality varies from perfect (consistency) to imperfect (robustness). This framework is particularly appealing in mechanism design contexts, where predictions might convey private information about the agents. In …

Signed Graph Representation Learning: A Survey
Zeyu Zhang, Peiyao Zhao, Xin Li, Jiamou Liu, Xinrui Zhang, Junjie Huang, Xiaofeng Zhu
https://arxiv.org/abs/2402.15980

Signed Graph Representation Learning: A Survey
With the prevalence of social media, the connectedness between people has been greatly enhanced. Real-world relations between users on social media are often not limited to expressing positive ties such as friendship, trust, and agreement, but they also reflect negative ties such as enmity, mistrust, and disagreement, which can be well modelled by signed graphs. Signed Graph Representation Learning (SGRL) is an effective approach to analyze the complex patterns in real-world signed graphs with …

Verifying message-passing neural networks via topology-based bounds tightening
Christopher Hojny, Shiqiang Zhang, Juan S. Campos, Ruth Misener
https://arxiv.org/abs/2402.13937

Verifying message-passing neural networks via topology-based bounds tightening
Since graph neural networks (GNNs) are often vulnerable to attack, we need to know when we can trust them. We develop a computationally effective approach towards providing robust certificates for message-passing neural networks (MPNNs) using a Rectified Linear Unit (ReLU) activation function. Because our work builds on mixed-integer optimization, it encodes a wide variety of subproblems, for example it admits (i) both adding and removing edges, (ii) both global and local budgets, and (iii) bot…

To Trust or Not to Trust: Assignment Mechanisms with Predictions in the Private Graph Model
Riccardo Colini-Baldeschi, Sophie Klumper, Guido Sch\"afer, Artem Tsikiridis
https://arxiv.org/abs/2403.03725

To Trust or Not to Trust: Assignment Mechanisms with Predictions in the Private Graph Model
The realm of algorithms with predictions has led to the development of several new algorithms that leverage (potentially erroneous) predictions to enhance their performance guarantees. The challenge is to devise algorithms that achieve optimal approximation guarantees as the prediction quality varies from perfect (consistency) to imperfect (robustness). This framework is particularly appealing in mechanism design contexts, where predictions might convey private information about the agents. In …

This https://arxiv.org/abs/2306.04366 has been replaced.
initial toot: https://mastoxiv.page/@arXiv_csSI_…

Enhancing Worker Recruitment in Collaborative Mobile Crowdsourcing: A Graph Neural Network Trust Evaluation Approach
Collaborative Mobile Crowdsourcing (CMCS) allows platforms to recruit worker teams to collaboratively execute complex sensing tasks. The efficiency of such collaborations could be influenced by trust relationships among workers. To obtain the asymmetric trust values among all workers in the social network, the Trust Reinforcement Evaluation Framework (TREF) based on Graph Convolutional Neural Networks (GCNs) is proposed in this paper. The task completion effect is comprehensively calculated by …

On Discrete Subproblems in Integer Optimal Control with Total Variation Regularization in Two Dimensions
Paul Manns, Marvin Severitt
https://arxiv.org/abs/2403.09213

On Discrete Subproblems in Integer Optimal Control with Total Variation Regularization in Two Dimensions
We analyze integer linear programs which we obtain after discretizing two-dimensional subproblems arising from a trust-region algorithm for mixed integer optimal control problems with total variation regularization. We discuss NP-hardness of the discretized problems and the connection to graph-based problems. We show that the underlying polyhedron exhibits structural restrictions in its vertices with regards to which variables can attain fractional values at the same time. Based on this propert…

Unsupervised Social Bot Detection via Structural Information Theory
Hao Peng, Jingyun Zhang, Xiang Huang, Zhifeng Hao, Angsheng Li, Zhengtao Yu, Philip S. Yu
https://arxiv.org/abs/2404.13595

Unsupervised Social Bot Detection via Structural Information Theory
Research on social bot detection plays a crucial role in maintaining the order and reliability of information dissemination while increasing trust in social interactions. The current mainstream social bot detection models rely on black-box neural network technology, e.g., Graph Neural Network, Transformer, etc., which lacks interpretability. In this work, we present UnDBot, a novel unsupervised, interpretable, yet effective and practical framework for detecting social bots. This framework is bu…

Commute with Community: Enhancing Shared Travel through Social Networks
Tian Siyuan, Dai Renjie, Wang Junhao, He Zhengxiao
https://arxiv.org/abs/2404.05987

Commute with Community: Enhancing Shared Travel through Social Networks
Shared mobility redefines urban transportation, offering economic and environmental benefits by reducing pollution and urban congestion. However, in the post-pandemic era, the shared mobility sector is grappling with a crisis of trust, particularly concerning passenger hesistancy towards shared transportation options. To address these problems, in this paper we take social network into consideration and propose a novel carpooling matching framework based on graph neural network and reinforcemen…