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A list of all the posts and pages found on the site. For you robots out there is an XML version available for digesting as well.

Pages

Posts

Future Blog Post

less than 1 minute read

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This post will show up by default. To disable scheduling of future posts, edit config.yml and set future: false.

Blog Post number 4

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This is a sample blog post. Lorem ipsum I can’t remember the rest of lorem ipsum and don’t have an internet connection right now. Testing testing testing this blog post. Blog posts are cool.

Blog Post number 3

less than 1 minute read

Published:

This is a sample blog post. Lorem ipsum I can’t remember the rest of lorem ipsum and don’t have an internet connection right now. Testing testing testing this blog post. Blog posts are cool.

Blog Post number 2

less than 1 minute read

Published:

This is a sample blog post. Lorem ipsum I can’t remember the rest of lorem ipsum and don’t have an internet connection right now. Testing testing testing this blog post. Blog posts are cool.

Blog Post number 1

less than 1 minute read

Published:

This is a sample blog post. Lorem ipsum I can’t remember the rest of lorem ipsum and don’t have an internet connection right now. Testing testing testing this blog post. Blog posts are cool.

publications

Peer Filtering: Democratic Misinformation Control in Social Networks

This work is about the phenomon of peer filtering, a natural effect where the sharing dynamics of users in a social platform increases the porportion of true content that survives in the network relative to the amount that is initially incoming. Download paper here Presented at: 2024 INFORMS Conference, 2024 International School and Conference on Network Science(NetSci), 2024 Conference on Network Science and Economics

Work in Progress: Graph Anonymization

This work considers the following problem: We have a data consisting of an unidrected network with labelled nodes. We want to be able to realize a unlabeled possibly permuted version of the network publically with revealing details about the network(known as the published graph). The best way to describe the privacy goals is to consider an attacker who has the published graph, the list of ids in the network, and another network they learned outside what we gave them. We call this second graph the crawled graph. The goal of the attack then is to use these two graphs to uncover the ids of nodes in the network. Our work focuses on possible defenses against such attacks while preserving other useful information about the network ex. the degree distribution of the true hidden network and the published graph should be similar.