The DOI matches its Crossref record; 20 of the 20 references it cites were found in a registry or an index.
R-MAT: A Recursive Model for Graph Mining
Open accessAbstract
How does a ‘normal’ computer (or social) network look like? How can we spot ‘abnormal’ sub-networks in the Internet, or web graph? The answer to such questions is vital for outlier detection (terrorist networks, or illegal money-laundering rings), forecasting, and simulations (“how will a computer virus spread?”). The heart of the problem is finding the properties of real graphs that seem to persist over multiple disciplines. We list such “laws” and, more importantly, we propose a simple, parsimonious model, the “recursive matrix” (R-MAT) model, which can quickly generate realistic graphs, capturing the essence of each graph in only a few parameters. Contrary to existing generators, our model can trivially generate weighted, directed and bipartite graphs; it subsumes the celebrated Erdős-Rényi model as a special case; it can match the power law behaviors, as well as the deviations from them (like the “winner does not take it all” model of Pennock et al. [21]). We present results on multiple, large real graphs, where we show that our parameter fitting algorithm (AutoMAT-fast) fits them very well.
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Cites
Statistical mechanics of complex networks
Albert, R., & Barabási, A.-L. (2002) · Reviews of Modern Physics · doi:10.1103/revmodphys.74.47 · Cited by 20,471
The Structure and Function of Complex Networks
Newman, M. E. J. (2003) · SIAM Review · doi:10.1137/s003614450342480 · Cited by 18,719
Epidemic Spreading in Scale-Free Networks
Pastor-Satorras, R., & Vespignani, A. (2001) · Physical Review Letters · doi:10.1103/physrevlett.86.3200 · Cited by 5,731
On power-law relationships of the Internet topology
Faloutsos, M., Faloutsos, P., & Faloutsos, C. (1999) · Proceedings of the conference on Applications, technologies, architectures, and protocols for computer communication · doi:10.1145/316188.316229 · Cited by 4,196
Self-citationLinked: The New Science of Networks
Barabási, A.-L., & Aldana-Gonzalez, M. (2003) · Physics Today · doi:10.1063/1.1570778 · Cited by 2,724
The Web as a Graph: Measurements, Models, and Methods
Kleinberg, J. M., Kumar, R., Raghavan, P., Rajagopalan, S., & Tomkins, A. S. (1999) · Lecture Notes in Computer Science · doi:10.1007/3-540-48686-0_1 · Cited by 1,016
Inferring Web communities from link topology
Gibson, D., Kleinberg, J., & Raghavan, P. (1998) · Proceedings of the ninth ACM conference on Hypertext and hypermedia : links, objects, time and space---structure in hypermedia systems links, objects, time and space---structure in hypermedia systems - HYPERTEXT '98 · doi:10.1145/276627.276652 · Cited by 768
Mining the Web: Discovering Knowledge from Hypertext Data
Chakrabarti, S. (2002) · Thammasat University Digital Collections · Cited by 696
Džeroski, S. (2006) · Data Mining and Knowledge Discovery Handbook · doi:10.1007/0-387-25465-x_41 · Cited by 520
On distinguishing between Internet power law topology generators
Bu, T., & Towsley, D. (2003) · Proceedings.Twenty-First Annual Joint Conference of the IEEE Computer and Communications Societies · doi:10.1109/infcom.2002.1019309 · Cited by 516
Cited by
Graphs over time: densification laws, shrinking diameters and possible explanations
Leskovec, J., Kleinberg, J., & Faloutsos, C. (2005) · Proceedings of the eleventh ACM SIGKDD international conference on Knowledge discovery in data mining · doi:10.1145/1081870.1081893 · Cited by 2,281
Self-citationA Survey of Statistical Network Models
Goldenberg, A., Zheng, A. X., Fienberg, S. E., & Airoldi, E. M. (2010) · Foundations and Trends® in Machine Learning · doi:10.1561/2200000005 · Cited by 819
Frieze, A., & Karoński, M. (2015) · doi:10.1017/cbo9781316339831 · Cited by 576
Trinity: a distributed graph engine on a memory cloud
Shao, B., Wang, H., & Li, Y. (2013) · Proceedings of the 2013 ACM SIGMOD International Conference on Management of Data · doi:10.1145/2463676.2467799 · Cited by 432
Accelerating CUDA graph algorithms at maximum warp
Hong, S., Kim, S. K., Oguntebi, T., & Olukotun, K. (2011) · Proceedings of the 16th ACM symposium on Principles and practice of parallel programming · doi:10.1145/1941553.1941590 · Cited by 339
Metadata
- Authors
- Chakrabarti, D., Zhan, Y., & Faloutsos, C.
- Venue
- Proceedings of the 2004 SIAM International Conference on Data Mining
- Published
- 2004-04-22
- Pages
- 442-446
- OpenAlex
- W1482680420
- Cited by
- 1,255
- References
- 20
- FWCI
- 20.71
- Type
- conference paper
- Language
- en
- Open access
- green
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