The DOI matches its Crossref record; 7 of the 7 references it cites were found in a registry or an index.
Heat kernel based community detection
Abstract
The heat kernel is a type of graph diffusion that, like the much-used personalized PageRank diffusion, is useful in identifying a community nearby a starting seed node. We present the first deterministic, local algorithm to compute this diffusion and use that algorithm to study the communities that it produces. Our algorithm is formally a relaxation method for solving a linear system to estimate the matrix exponential in a degree-weighted norm. We prove that this algorithm stays localized in a large graph and has a worst-case constant runtime that depends only on the parameters of the diffusion, not the size of the graph. On large graphs, our experiments indicate that the communities produced by this method have better conductance than those produced by PageRank, although they take slightly longer to compute. On a real-world community identification task, the heat kernel communities perform better than those from the PageRank diffusion.
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Cites
Modularity and community structure in networks
Newman, M. E. J. (2006) · Proceedings of the National Academy of Sciences · doi:10.1073/pnas.0601602103 · Cited by 12,256
What is Twitter, a social network or a news media?
Kwak, H., Lee, C., Park, H., & Moon, S. (2010) · Proceedings of the 19th international conference on World wide web · doi:10.1145/1772690.1772751 · Cited by 6,652
Measurement and analysis of online social networks
Mislove, A., Marcon, M., Gummadi, K. P., Druschel, P., & Bhattacharjee, B. (2007) · Proceedings of the 7th ACM SIGCOMM conference on Internet measurement · doi:10.1145/1298306.1298311 · Cited by 3,057
Nineteen Dubious Ways to Compute the Exponential of a Matrix, Twenty-Five Years Later
Moler, C., & Van Loan, C. (2003) · SIAM Review · doi:10.1137/s00361445024180 · Cited by 2,357
Functions of Matrices: Theory and Computation
Higham, N. J. (2008) · doi:10.1137/1.9780898717778 · Cited by 2,113
Local Graph Partitioning using PageRank Vectors
Andersen, R., Chung, F., & Lang, K. (2006) · 2006 47th Annual IEEE Symposium on Foundations of Computer Science (FOCS'06) · doi:10.1109/focs.2006.44 · Cited by 967
Monte Carlo Methods in PageRank Computation: When One Iteration is Sufficient
Avrachenkov, K., Litvak, N., Nemirovsky, D., & Osipova, N. (2007) · SIAM Journal on Numerical Analysis · doi:10.1137/050643799 · Cited by 250 · cited 1×
“[13] to achieve a randomized sublinear time algorithm to estimate the largest PageRank entries, and in fact, Monto Carlo methods frequently feature in PageRank computations due to the relationship between the diffusion and a random walk [7, 13, 8, 9].”
Method
Cited by
Random walks and diffusion on networks
Masuda, N., Porter, M. A., & Lambiotte, R. (2017) · Physics Reports · doi:10.1016/j.physrep.2017.07.007 · Cited by 657
Metadata
- Authors
- Kloster, K., & Gleich, D. F.
- Venue
- Proceedings of the 20th ACM SIGKDD international conference on Knowledge discovery and data mining
- Publisher
- ACM
- Published
- 2014-08-24
- Pages
- 1386-1395
- OpenAlex
- W2016273060
- Cited by
- 205
- References
- 7
- FWCI
- 13.04
- Type
- conference paper
- Language
- en
- Open access
- closed
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