Data Skeptic The Mystery Behind Large Graphs
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Jan 10, 2025 David Tench, a Grace Hopper postdoctoral fellow at Lawrence Berkeley National Labs, specializes in scalable graph algorithms. He discusses how his techniques enable real-time analysis of massive datasets while reducing storage needs. David challenges the idea that large graphs are typically sparse, suggesting a potential bias in data analysis processes. He emphasizes the importance of context in network analysis and introduces innovative approaches like CubeSketch and Graph Zeppelin to enhance computation efficiency in handling complex graphs.
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Deep Web
- The internet's giant connected component (GCC) may only be 5% of the entire web.
- The remaining portion is referred to as the 'deep web', which search engines cannot index.
Negative Influencer
- David Tench analyzed a car company's brand and discovered a negative influencer.
- This influencer's tweets, despite being relevant to the brand, added a negative context.
Graph Density Bias
- Challenge the assumption that large graphs are always sparse.
- Consider the possibility of a bias due to limitations in analyzing large dense graphs.