Gossip: Identifying Central Individuals in a Social Network

A dataset from paperswithcode, authored by Esther Duflo, focused on social network analysis. The title suggests it contains data for identifying central individuals, likely within a network where gossip is a key dynamic. The specific scale, temporal coverage, and data collection method are not detailed in the available metadata.

Use Cases

• Benchmarking centrality algorithms on a real-world social graph (inferred from domain, verify after download)
• Modeling the role of gossip in information diffusion networks (inferred from domain, verify after download)
• Analyzing the relationship between network position and social behavior (inferred from domain, verify after download)

Strengths

• Published on paperswithcode, a platform for linking datasets with research papers.
• Authored by a notable researcher, Esther Duflo.

Limitations

• Metadata is minimal; actual content requires verification after download.
• Column-level documentation is absent; field semantics must be inferred after download.
• Row count, file formats, and last update date are unknown.

Provenance

Source

paperswithcode

Collection Method

How gathered is unknown.

Time Range

Temporal coverage is unknown.

Freshness

Last update date is unknown; freshness unverified.

Geography

Spatial coverage is unknown.