Managing and Mining Large Graphs: Patterns and Algorithms
Summary: Patterns in real-world static, weighted, and dynamic graphs; approaches to scale to graphs with billions of nodes and edges. Reviews of tools for large-graph mining (SVD, Hadoop) and a practical blueprint for designing and implementing scalable graph-mining algorithms on Hadoop. (summarized by gpt-5-nano on Feb 09 2026)
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Authors
- 1. Christos Faloutsos (Carnegie Mellon University)
- 2. U Kang (Carnegie Mellon University)
BibTeX Citation
@inproceedings{faloutsos_sigmod12,
title = {{Managing and Mining Large Graphs: Patterns and Algorithms}},
author = {Faloutsos, Christos and Kang, U},
series = {{SIGMOD} '12},
booktitle = {Proceedings of the {ACM} {SIGMOD} International Conference on Management of Data},
publisher = {Association for Computing Machinery},
doi = {10.1145/2213836.2213906},
url = {https://dl.acm.org/doi/10.1145/2213836.2213906},
year = {2012}
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