By Anthony Bonato, Fan Chung Graham, Pawel Pralat
This e-book constitutes the complaints of the thirteenth overseas Workshop on Algorithms and types for the internet Graph, WAW 2016, held in Montreal, quality control, Canada, in December 2016.
The thirteen complete papers offered during this quantity have been conscientiously reviewed and chosen from 14 submissions. The workshop accumulated the researchers who're engaged on graph-theoretic and algorithmic features of comparable complicated networks, together with social networks, quotation networks, organic networks, molecular networks, and different networks coming up from the Internet.
Read or Download Algorithms and Models for the Web Graph: 13th International Workshop, WAW 2016, Montreal, QC, Canada, December 14–15, 2016, Proceedings PDF
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Extra info for Algorithms and Models for the Web Graph: 13th International Workshop, WAW 2016, Montreal, QC, Canada, December 14–15, 2016, Proceedings
School of computer Science, Carnegie-Mellon University, Pittsburgh, PA (1998) 11. : An experimental investigation of kernels on graphs for collaborative recommendation and semisupervised classiﬁcation. Neural Netw. 31, 53–72 (2012) 12. : Community structure in social and biological networks. PNAS USA 99, 7821–7826 (2002) 13. : Using local spectral methods to robustify graphbased learning algorithms. In: Proceedings of ACM SIGKDD (2015) 14. : Randomized iterative methods for linear systems. SIAM J.
Appl. 15(2), 262–278 (2009) 23. : New regularized algorithms for transductive learning. , Shawe-Taylor, J. ) ECML PKDD 2009. LNCS (LNAI), vol. 5782, pp. 442–457. Springer, Heidelberg (2009). 1007/978-3-642-04174-7 29 24. : Online semi-supervised learning on quantized graphs. In: Proceedings of UAI (2010) 25. : Learning with local and global consistency. Adv. Neural Inf. Process. Syst. 16, 321–328 (2004) 26. : Spectral clustering and transductive learning with multiple views. In: Proceedings of ICML (2007) 27.
We propose two asynchronously distributed approaches for graph-based semi-supervised learning. The ﬁrst approach is based on stochastic approximation, whereas the second approach is based on randomized Kaczmarz algorithm. In addition to the possibility of distributed implementation, both approaches can be naturally applied online to streaming data. We analyse both approaches theoretically and by experiments. It appears that there is no clear winner and we provide indications about cases of superiority for each approach.