Tootfinder

reality_mining: Reality mining proximity network (2004)
A network of human proximities among students at Massachusetts Institute of Technology (MIT), as measured by personal mobile phones. Nodes represent people (students from the Media Lab and the Sloan Business School) and an edge connects a pair if the two devices made a Bluetooth handshake at the time. Edges are timestamped.
This network has 96 nodes and 1086404 edges.
Tags: Social, Offline, Unweighted, Timestamps

reality_mining: Reality mining proximity network (2004)
A network of human proximities among students at Massachusetts Institute of Technology (MIT), as measured by personal mobile phones. Nodes represent people (students from the Media Lab and the Sloan Business School) and an edge connects a pair if the two devices made a Bluetooth handshake at the time. Edges are timestamped.
This network has 96 nodes and 1086404 edges.
Tags: Social, Offline, Unweighted, Timestamps

Optimal Feedback Control in Social Networks in a McKean-Vlasov-Friedkin-Johnsen System
Paramahansa Pramanik
https://arxiv.org/abs/2508.17138 https://arxiv.…

Optimal Feedback Control in Social Networks in a McKean-Vlasov-Friedkin-Johnsen System
This paper presents a comprehensive analytical formulation for deriving a closed-form optimal strategy for agents operating within a social network, modeled through a McKean-Vlasov stochastic differential equation (SDE). Each agent aims to minimize a personal dynamic cost functional that accounts for deviations from the collective opinions of others, their own past beliefs, and is influenced by randomness and inherent opinion rigidity, often described as stubbornness. To tackle this, we develop…

reality_mining: Reality mining proximity network (2004)
A network of human proximities among students at Massachusetts Institute of Technology (MIT), as measured by personal mobile phones. Nodes represent people (students from the Media Lab and the Sloan Business School) and an edge connects a pair if the two devices made a Bluetooth handshake at the time. Edges are timestamped.
This network has 96 nodes and 1086404 edges.
Tags: Social, Offline, Unweighted, Timestamps

EVOLVE-X: Embedding Fusion and Language Prompting for User Evolution Forecasting on Social Media
Ismail Hossain, Sai Puppala, Md Jahangir Alam, Sajedul Talukder
https://arxiv.org/abs/2507.16847

EVOLVE-X: Embedding Fusion and Language Prompting for User Evolution Forecasting on Social Media
Social media platforms serve as a significant medium for sharing personal emotions, daily activities, and various life events, ensuring individuals stay informed about the latest developments. From the initiation of an account, users progressively expand their circle of friends or followers, engaging actively by posting, commenting, and sharing content. Over time, user behavior on these platforms evolves, influenced by demographic attributes and the networks they form. In this study, we present…

reality_mining: Reality mining proximity network (2004)
A network of human proximities among students at Massachusetts Institute of Technology (MIT), as measured by personal mobile phones. Nodes represent people (students from the Media Lab and the Sloan Business School) and an edge connects a pair if the two devices made a Bluetooth handshake at the time. Edges are timestamped.
This network has 96 nodes and 1086404 edges.
Tags: Social, Offline, Unweighted, Timestamps

reality_mining: Reality mining proximity network (2004)
A network of human proximities among students at Massachusetts Institute of Technology (MIT), as measured by personal mobile phones. Nodes represent people (students from the Media Lab and the Sloan Business School) and an edge connects a pair if the two devices made a Bluetooth handshake at the time. Edges are timestamped.
This network has 96 nodes and 1086404 edges.
Tags: Social, Offline, Unweighted, Timestamps

reality_mining: Reality mining proximity network (2004)
A network of human proximities among students at Massachusetts Institute of Technology (MIT), as measured by personal mobile phones. Nodes represent people (students from the Media Lab and the Sloan Business School) and an edge connects a pair if the two devices made a Bluetooth handshake at the time. Edges are timestamped.
This network has 96 nodes and 1086404 edges.
Tags: Social, Offline, Unweighted, Timestamps

reality_mining: Reality mining proximity network (2004)
A network of human proximities among students at Massachusetts Institute of Technology (MIT), as measured by personal mobile phones. Nodes represent people (students from the Media Lab and the Sloan Business School) and an edge connects a pair if the two devices made a Bluetooth handshake at the time. Edges are timestamped.
This network has 96 nodes and 1086404 edges.
Tags: Social, Offline, Unweighted, Timestamps

This https://arxiv.org/abs/2311.16139 has been replaced.
initial toot: https://mastoxiv.page/@arXiv_csCR_…

GNNBleed: Inference Attacks to Unveil Private Edges in Graphs with Realistic Access to GNN Models
Graph Neural Networks (GNNs) have become indispensable tools for learning from graph structured data, catering to various applications such as social network analysis and fraud detection for financial services. At the heart of these networks are the edges, which are crucial in guiding GNN models' predictions. In many scenarios, these edges represent sensitive information, such as personal associations or financial dealings, which require privacy assurance. However, their contributions to GNN mo…

Designing for Community Care: Reimagining Support for Equity & Well-being in Academia
Beatriz Severes, Ana O. Henriques, Rory Clark, Paulo Bala, Anna Carter, Rua Mae Williams, Geraldine Fitzpatrick
https://arxiv.org/abs/2507.01690

Designing for Community Care: Reimagining Support for Equity & Well-being in Academia
Academic well-being is deeply influenced by peer-support networks, yet they remain informal, inequitable, and unsustainable, often relying on personal connections and social capital rather than structured, inclusive systems. Additionally, institutional well-being responses frequently focus on student populations, neglecting the emotional labour of faculty and staff, reinforcing an exclusionary academic culture. Drawing on HCI methodologies, participatory design, and care ethics, this workshop w…

The Impact of COVID-19 on Twitter Ego Networks: Structure, Sentiment, and Topics
Kamer Cekini, Elisabetta Biondi, Chiara Boldrini, Andrea Passarella, Marco Conti
https://arxiv.org/abs/2506.03788

The Impact of COVID-19 on Twitter Ego Networks: Structure, Sentiment, and Topics
Lockdown measures, implemented by governments during the initial phases of the COVID-19 pandemic to reduce physical contact and limit viral spread, imposed significant restrictions on in-person social interactions. Consequently, individuals turned to online social platforms to maintain connections. Ego networks, which model the organization of personal relationships according to human cognitive constraints on managing meaningful interactions, provide a framework for analyzing such dynamics. The…

reality_mining: Reality mining proximity network (2004)
A network of human proximities among students at Massachusetts Institute of Technology (MIT), as measured by personal mobile phones. Nodes represent people (students from the Media Lab and the Sloan Business School) and an edge connects a pair if the two devices made a Bluetooth handshake at the time. Edges are timestamped.
This network has 96 nodes and 1086404 edges.
Tags: Social, Offline, Unweighted, Timestamps

reality_mining: Reality mining proximity network (2004)
A network of human proximities among students at Massachusetts Institute of Technology (MIT), as measured by personal mobile phones. Nodes represent people (students from the Media Lab and the Sloan Business School) and an edge connects a pair if the two devices made a Bluetooth handshake at the time. Edges are timestamped.
This network has 96 nodes and 1086404 edges.
Tags: Social, Offline, Unweighted, Timestamps

reality_mining: Reality mining proximity network (2004)
A network of human proximities among students at Massachusetts Institute of Technology (MIT), as measured by personal mobile phones. Nodes represent people (students from the Media Lab and the Sloan Business School) and an edge connects a pair if the two devices made a Bluetooth handshake at the time. Edges are timestamped.
This network has 96 nodes and 1086404 edges.
Tags: Social, Offline, Unweighted, Timestamps