Papers with network science
Multi-view Models for Political Ideology Detection of News Articles (D18-1)
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| Challenge: | Existing models for automatic detection of political ideology only leverage textual cues to identify the ideology evinced by a news article. |
| Approach: | They propose a novel attention based multi-view model that leverages cues from textual content and the network structure of news articles to identify political ideology. |
| Outcome: | The proposed model outperforms state of the art models by 10 percentage points on a battery of baselines and compares with baselines. |
Demystifying the Power of Large Language Models in Graph Generation (2025.findings-naacl)
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Yu Wang, Ryan A. Rossi, Namyong Park, Nesreen K. Ahmed, Danai Koutra, Franck Dernoncourt, Tyler Derr
| Challenge: | Large Language Models (LLMs) have been used for graph discriminative tasks, but their potential for graph structure generation remains unexplored. |
| Approach: | They propose to use LLMs to generate graphs that optimize network properties by injecting domain expertise from network science into the code. |
| Outcome: | The proposed model generates graphs satisfying each property in different domains and compares it with established graph generative models across multiple domains. |